US Department of Transportation
L o a d i n g
The mission of the United States Department of Transportation is to deliver the world’s leading transportation system, serving the American people and economy through the safe, efficient, sustainable, and equitable movement of people and goods.
Available DatasetsShowing 6851 of 6851 results
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland 2019 Average Annual Daily Traffic data1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data without rows where road_tmc and road are inconsistent.1last year
- Annual length of U.S. public roads in miles by functional system for each of the 50 states, DC, and Puerto Rico from the Highway Statistics table HM-20. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors.)1last year
- The Monthly module includes a limited set of key indicators reported by transit properties. Data is reported on a monthly basis, by mode and type of service, for a fiscal year. The four data items included are: 1. Unlinked Passenger Trips 2. Vehicle Revenue Miles 3. Vehicle Revenue Hours 4. Vehicles Operated in Maximum Service (Peak Vehicles) This dataset presents these values in their own column in a long format (each row of the file is an individual Agency/Mode/TOS/Date). The data source is shared with the static Excel file hosted on the FTA website here: https://www.transit.dot.gov/ntd/data-product/monthly-module-adjusted-data-release. This dataset differs from the static Excel file in its formatting as well as being updated weekly, to capture data as it is reported and validated for a given publication month. Mode Codes: Alaska Railroad (AR) Cable Car (CC) Commuter Rail (CR) Heavy Rail (HR) Hybrid Rail (YR) Inclined Plane (IP) Light Rail (LR) Monorail/Automated Guideway (MG) Streetcar Rail (SR) Aerial Tramway (TR) Commuter Bus (CB) Bus (MB) Bus Rapid Transit (RB) Demand Response (DR) Ferryboat (FB) Jitney (JT) Público (PB) Trolleybus (TB) Vanpool (VP) Mode and Type of Service Changes and Impacts on this Time Series: "Monthly data are reported by mode and type of service. From 2002 through 2011, there were 16 modes in the NTD. NTD monthly ridership data is now reported according to refined modal classifications. Service previously reported as bus (MB) now may be reported as either MB, Commuter Bus (CB), or Bus Rapid Transit (RB). Additionally, service previously categorized as Light Rail (LR) now may be reported as LR or Streetcar (SR). Similarly, Types of Service were refined in Report Year 2019. From 2002 - 2018, there were two types of service: Directly Operated (DO) and Purchased Transportation (PT). As of 2019, Purchased Transportation is now classified such that agencies report the purchased transportation based on the type of contractor: general third party (PT), taxicab operator (TX), or transportation network company (TN). FTA concurrently removed the ""Demand Response Taxi"" (DT/PT) mode in 2019. FTA now considers all such service as Demand Response (DR) with Taxi (TX) type of service and this time series has been updated to reflect this change. " For more information on this dataset, please consult the full Read Me in the attached file.1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is the processed integrated traffic data with work zone and incident information. Attached below are the number of lanes data and impacted work zone .pkl file.1last year
- Historic Highway Performance Monitoring System sample data for the year 19991last year
- Historic Highway Performance Monitoring System sample data for the year 19901last year
- Historic Highway Performance Monitoring System sample data for the year 19941last year
- Historic Highway Performance Monitoring System universe data for the year 19851last year
- Historic Highway Performance Monitoring System universe data for the year 20041last year
- Historic Highway Performance Monitoring System universe data for the year 19961last year
- Historic Highway Performance Monitoring System sample data for the year 19961last year
- Historic Highway Performance Monitoring System sample data for the year 19911last year
- Historic Highway Performance Monitoring System sample data for the year 19931last year
- Historic Highway Performance Monitoring System universe data for the year 19921last year
- Historic Highway Performance Monitoring System sample data for the year 19921last year
- Historic Highway Performance Monitoring System universe data for the year 20021last year
- Historic Highway Performance Monitoring System universe data for the year 19871last year
- Historic Highway Performance Monitoring System universe data for the year 19911last year
- Historic Highway Performance Monitoring System universe data for the year 20061last year
- Historic Highway Performance Monitoring System universe data for the year 19981last year
- Historic Highway Performance Monitoring System universe data for the year 20051last year
- Historic Highway Performance Monitoring System universe data for the year 19951last year
- Historic Highway Performance Monitoring System universe data for the year 19971last year
- Historic Highway Performance Monitoring System universe data for the year 19901last year
- Historic Highway Performance Monitoring System sample data for the year 19891last year
- Historic Highway Performance Monitoring System sample data for the year 19881last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is a raw sample of Maryland roadway speed data1last year
- Historic Highway Performance Monitoring System universe data for the year 20001last year
- Historic Highway Performance Monitoring System universe data for the year 19931last year
- Historic Highway Performance Monitoring System universe data for the year 19861last year
- Historic Highway Performance Monitoring System sample data for the year 19801last year
- last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 20031last year
- Historic Highway Performance Monitoring System sample data for the year 19951last year
- Historic Highway Performance Monitoring System universe data for the year 20071last year
- Annual state-reported motor vehicle registration data for the 50 states, DC, and Puerto Rico (2000 - 2010) reported in Highway Statistics table MV-1.1last year
- last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 20001last year
- last year
- last year
- 1last year
- Historic Highway Performance Monitoring System sample data for the year 20011last year
- Historic Highway Performance Monitoring System universe data for the year 19941last year
- last year
- last year
- 1last year
- last year
- State DOT will provide VMT. This data is summarized by Paved and Unpaved and by Vehicle Type.1last year
- Historic Highway Performance Monitoring System universe data for the year 20011last year
- last year
- last year
- 1last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 19891last year
- last year
- 1last year
- last year
- last year
- last year
- State DOT will provide Local Vehicle-Miles-Traveled (VMT) summarized by FHWA Adjusted Urban Area.1last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 19881last year
- last year
- last year
- State DOT will provide Local Vehicle-Miles-Traveled (VMT) summarized by FHWA Adjusted Urban Area.1last year
- Historic Highway Performance Monitoring System universe data for the year 20081last year
- 1last year
- Test case WFCW-1 Results - FCW Stopped Vehicle Rep 21last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel.1last year
- State DOT will provide VMT. This data is summarized by Paved and Unpaved and by Vehicle Type.1last year
- This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.1last year
- Annual lane miles in the National Highway System by rural / urban and functional system for the 50 states, DC, and Puerto Rico from the Highway Statistics table HM-43. (Note: In 2009, Functional System changed attributes.)1last year
- Historic Highway Performance Monitoring System universe data for the year 19991last year
- WFCW-2 Stopped Vehicle Message Prioritization Rep 21last year
- Miles of public roads by the roadway's ownership and whether rural or urban for the 50 states, DC, and Puerto Rico from FHWA Highway Statistics table HM-10. (Note: In 2011, Ownership added a new value for "unreported.")1last year
- Annual dollars of federal highway trust fund receipts from various tax sources for each of the 50 states and DC from the Highway Statistics table FE-9. (Note: Tax type for "Gasohol" only from 1999 to 2005.)1last year
- Historic Highway Performance Monitoring System sample data for the year 19861last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The North contains data for the following States: Maine, New Hampshire, Vermont, New York, Massachusetts, Rhode Island, Connecticut, New Jersey, Pennsylvania, Ohio, Maryland, District of Columbia, and Delaware.1last year
- The table displays the total number of licensed drivers in each State. The table shows the number of male and female licensed drivers by sex and age group.1last year
- Historic Highway Performance Monitoring System universe data for the year 19831last year
- Historic Highway Performance Monitoring System data sample for the year 19981last year
- Annual state highway agency dollars of capital outlays by rural / urban, functional class, and improvement type for the 50 states and DC from the Highway Statistics table SF-12A.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin.1last year
- State DOTs will provide Local and Rural Minor Collector Mileage summarized by county, ownership, and Paved and Unpaved. This data is provided in a direct input by the State DOTs.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The South contains data for the following States: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia, and Puerto Rico.1last year
- last year
- last year
- 1last year
- The Facility Inventory dataset details all facilities supporting public transit service as reported to the National Transit Database (NTD) by each public transit agency in the 2023 report year. This file is also published at https://www.transit.dot.gov/ntd/ntd-data, under the Product Type of "Annual Database (Excel)." Equivalent datasets from 2018 through 2022 can also be found using that link. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- The Summary Statistics dashboard includes rural and urban measures for roadway mileage, lane miles, vehicle miles traveled, fatalities, and fatality rate.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The South contains data for the following States: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia, and Puerto Rico.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The North contains data for the following States: Maine, New Hampshire, Vermont, New York, Massachusetts, Rhode Island, Connecticut, New Jersey, Pennsylvania, Ohio, Maryland, District of Columbia, and Delaware.1last year
- Annual estimated length of lane miles by federal-aid system and rural /urban for the 50 States, DC, and Puerto Rico from the Highway Statistics table HM-48.1last year
- This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.1last year
- last year
- Summary monthly motor fuel data on the amount of on-highway fuel used at the national level. Includes the amount of gallons of gasoline/gasohol and special fuel (primarily diesel) taxed each month.1last year
- Summary monthly motor fuel data on the amount of on-highway fuel used at the state level. Includes the amount of gallons of gasoline/gasohol and special fuel (primarily diesel) taxed each month.1last year
- Historic Highway Performance Monitoring System universe data for the year 19801last year
- The main dataset is a 70 MB file of trajectory data (I294_L1_final.csv) that contains position, speed, and acceleration data for small and large automated (L1) vehicles and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for ten distinct data collection “Runs” (I294_L1_RunX_with_lanes.png, where X equals 8, 18, and 20 for southbound runs and 1, 3, 7, 9, 11, 19, and 21 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L1-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L1.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L1_Lane-2.png through I294_L1_Lane-5.png and the northbound lanes are shown visually in I294_L1_Lane2.png through I294_L1_Lane5.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day. As part of this dataset, the following files were provided: I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X. I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane cent1last year
- The main dataset is a 350 MB file of trajectory data (TGSIM-Foggy Bottom-Data.csv) that contains position, speed, and acceleration data for pedestrians, bicycles, scooters, non-automated passenger cars, automated vehicles, motorcycles, buses, and trucks in an urban environment. Supporting files include an aerial reference image (Reference_Image_Foggy Bottom.png) and a list of polygon boundaries (Foggy_Bottom_boundaries.txt) and associated images (i1.png, i2.png, …, i49.png stored in the folder titled “Annotation on Regions.zip”) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset. As part of this dataset, the following files were provided: TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.0186613838586-meter conversion. Reference_Image_Foggy Bottom.png is the aerial reference image that defines the geographic region and the associated roadway segments. Foggy_Bottom_boundaries.txt contains the coordinates that define the roadway segments (n = 49). Each polygon is a list of four to six coordinate pairs measured in pixels (which can be converted to meters using the provided 1 pixel = 0.0186613838586-meter conversion), with (0,0) global reference coordinates at the top-left of the reference image. Annotation on Regions.zip, which includes i1.png, i2.png,..., i49.png, are images that visually map the road segment IDs (indicated by the number following the i in the file name) to the reference image.1last year
- Miles of public roads by the roadway's functional system and whether rural or urban for the 50 states, DC, and Puerto Rico (from 1996) from FHWA Highway Statistics table HM-60. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors.)1last year
- Historic Highway Performance Monitoring System sample data for the year 19831last year
- Counts of Non-Major Safety and Security Events are reported to the National Transit Database on a monthly basis, by transit agency and transit mode. These include minor fires on transit property requiring suppression, transit worker assaults not involving transport for medical attention, and other safety events that are not reportable as Major Events because a Major Event reporting threshold is not met (see Safety and Security Events dataset for a list of Major Events). This file includes event data reported to the National Transit Database (NTD) for Commuter Rail (CR) and Alaska Railroad (AR) modes, as well as Heavy Rail (HR) service reported for Port Authority Trans Hudson (NTD ID: 20098), Hybrid Rail (YR) service for the Tri-County Metropolitan Transportation District of Oregon (NTD ID: 00008), Hybrid Rail (YR) service for Denton County Transportation Authority (NTD ID: 60101), and Hybrid Rail (YR) service for Capital Metropolitan Transportation Authority (NTD ID: 60048). Because these services fall under the safety oversight of the Federal Railroad Administration, the agencies are not required to report Safety Events (e.g., collisions, derailments, etc.) to the Federal Transit Administration through the NTD. Security events occurring on transit-owned property for these entities are reported to NTD, but excluded from other files to preserve the integrity of those datasets. They are presented in this file for completeness and should be considered by any user attempting to understand the scope and scale of reportable Security Events reported by public transit operators.1last year
- Historic Highway Performance Monitoring System sample data for the year 1980.1last year
- last year
- last year
- The main dataset is a 232 MB file of trajectory data (I395-final.csv) that contains position, speed, and acceleration data for non-automated passenger cars, trucks, buses, and automated vehicles on an expressway within an urban environment. Supporting files include an aerial reference image (I395_ref_image.png) and a list of polygon boundaries (I395_boundaries.csv) and associated images (I395_lane-1, I395_lane-2, …, I395_lane-6) stored in a folder titled “Annotation on Regions.zip” to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns “x1” to “x5” represent the horizontal pixel values in the reference image, with “x1” being the leftmost boundary line and “x5” being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. The origin point of the reference image is located at the top left corner. The dataset defines five lanes with five boundaries. Lane -6 corresponds to the area to the left of “x1”. Lane -5 corresponds to the area between “x1” and “x2”, and so forth to the rightmost lane, which is defined by the area to the right of “x5” (Lane -2). Lane -1 refers to vehicles that go onto the shoulder of the merging lane (Lane -2), which are manually separated by watching the videos. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments. I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns "x1" to "x5" represent the horizontal pi1last year
- The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for six distinct data collection “Runs” (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each “Run” (I-90-stationary-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94Stationary.csv” for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X. I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and ve1last year
- Annual gallons of taxed motor fuel for the 50 states and DC from FHWA Highway Statistics table MF-202.1last year
- This dataset offers insight on weekly fluctuation of the gasoline product supply, which is an important part of any analysis of construction trends, materials and operating costs associated with highway repair and construction, and changes in traffic volume. These data come directly from the Energy Information Administration (EIA) website. The EIA publishes the average daily amount of gasoline supplied in barrels, which HPPI converts to an average number of gallons of gasoline per week.1last year
- This report contains tables and charts on the financial condition of the U.S. major airlines. All data presented in this financial and traffic review are derived from data reported to the U.S. Department of Transportation on Form 41 Schedules by Large Certificated Air Carriers. The data are presented on both a carrier group and an individual carrier basis, but the primary focus is on the individual major carrier and its performance. Data are presented for the most recent quarterly period and the comparable quarter a year earlier and also on a 12-month ended basis as at the end of the five most recent quarters. In addition, data on charges over comparable periods 12-months earlier are presented. A graphic presentation of comparative trends, on a carrier group basis, is made for several unit and overall financial indicators. In the case of merged carriers, data for the carriers involved have been combined and presented under the name of the surviving carrier so that meaningful comparisons could be made for the most recent 18 quarters. Also, carriers can move between groupings (Majors and Nationals) based on the criteria listed below over time. Each report includes 18 quarters of data. In the instance where a carrier falls into both groupings during the 18 quarters, a carrier will appear in both reports. The data from the Majors report and the data from the Nationals report should not be combined without ensuring any duplications are removed. Carrier Group Definitions Majors: Air carriers with annual operating revenues exceeding $1,000,000,000 Nationals: Air carriers with annual operating revenues between $100,000,000 and $1,000,000,000 Large Regionals: Air carriers with operating revenues between $20,000,000 and $99,000,000 Medium Regionals: Carriers with annual operating revenues less than $19,999,999 or that operate only aircraft with 60 seats or less (or 18,000 lbs maximum payload) https://www.transportation.gov/policy/aviation-policy/airline-quarterly-financial-review1last year
- Historic Highway Performance Monitoring System sample data for the year 19821last year
- Historic Highway Performance Monitoring System sample data for the year 19851last year
- last year
- This a reference table for the Grade Crossing Inventory System, which is the application used to submit data for the Highway-Rail Grade Crossing Inventory (Form 71). The data dictionary for GCIS is attached as well. The LookupType column contains the name of the field/column in the source GCIS/Form 71 dataset. The LookupValue column contains the submitted value and the LookupText field is the human-readable text description of that value (e.g. for LookupType=TypeXing; LookupValue=3 and LookupText=Public, which designates that a crossing is public). This reference table can be used for the Crossing Inventory Source Data Form 71 – Current: https://datahub.transportation.gov/dataset/Crossing-Inventory-Source-Data-Form-71-Current/xp92-5xme.1last year
- The main dataset is a 9 MB file of trajectory data (I294_L2_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for twelve distinct data collection “Runs” (I294_L2_Run_X_ref_image_with_lanes.png, where X equals 5, 28, 30, 36, 38, and 42 for southbound runs and 23, 29, 31, 33, 35, and 41 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L2-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L2.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L2_lane-2.png through I294_L2_lane-5.png and the northbound lanes are shown visually in I294_L2_lane2.png through I294_L2_lane5.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided: I294_L2_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the L2 test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I294_L2_Run_X_ref_image_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X. I294_L2_Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. T1last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 19841last year
- 2019 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- Annual number of licensed drivers for the 50 states and DC from FHWA Highway Statistics table DL-201.1last year
- last year
- This dataset details the ages of guideway elements for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Guideway elements include elements, structures, or facilities dedicated specifically to transit use, such as track, subway structures, tunnels, bridges, and propulsion power systems. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Way Mileage database files. In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the data tables from before 2015, you can find data on vehicles in the file called "Transit Way Mileage - Rail Modes." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- 2018 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- Historic Highway Performance Monitoring System sample data for the year 19801last year
- This dataset details funding from taxes levied by each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details state funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include General and Transportation funds. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In report year 2022, Extraordinary and Special Item Funds were reported under General Funds. In report year 2023, this was separated into its own category. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details directly generated funding for each agency. Examples include Fares, Concessions and Advertising. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- As of Report Year (RY) 2023, FTA requires that reporters with fixed route modes create and maintain a public domain general transit feed specification (GTFS) dataset that reflects their fixed route service. This specification allows for the mapping and other geospatial data visualization and analyses of key transit elements such as stops, routes, and trips. At least one GTFS weblink is provided by the transit agency for each fixed route bus mode and type of service. These include all Rail modes as well as Bus, Bus Rapid Transit, Commuter Bus, Ferryboat and Trolleybus. GTFS requires that an overarching compressed file contain, at a minimum, seven underlying text files: (a) Agency; (b) Stops; (c) Routes; (d) Trips; (e) Stop Times; (f) Calendar or Calendar Dates.txt; and (g) Feed Info.txt. An eighth file, Shapes.txt, is an optional file. FTA collects and publishes these links for further analysis using related GTFS files. FTA is not responsible for managing the websites that host these files, and users with questions regarding the GTFS data are encouraged to contact the transit agency. In many cases, publicly hosted weblinks could not be provided (i.e., due to constraints within the transit agency), but the agency was able to produce a zip file of the required GTFS data. Demand Response, Vanpool, and other non-fixed route modes are excluded. The column "Alternate Format" indicates that the agency provided FTA a weblink in an alternate format with some justification for doing so. The file "Waived" indicates that no GTFS files were produced and FTA granted the agency a waiver from the requirement in Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2023 General Transit Feed Specification database file. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Volume of taxed special fuel, primarily diesel, but including alternative fuels, reported by the States each month, based on reports from suppliers and distributors. These amounts are reported in various Office of Highway Policy Information (OHPI) products including the longstanding Monthly Motor Fuel Report, and the annual Highway Statistics publications.1last year
- last year
- 2020 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- This dataset details local funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Historic Highway Performance Monitoring System sample data for the year 20051last year
- Volume of gasoline reported by the States each month, based on reports from suppliers and distributors. These amounts are reported in various Office of Highway Policy Information (OHPI) products including the longstanding Monthly Motor Fuel Report, and the annual Highway Statistics publications.1last year
- Select summary highway statistics, 1980 - 2017, mileage, lane-miles, vehicle miles traveled, and fatalities by state and functional system.1last year
- This dataset details track and roadway mileage/characteristics for each agency, mode, and type of service, as reported to the National Transit Database in Report Years 2022 and 2023. These data include the types of track/roadway elements employed in transit operation, as well as the length and/or count of certain elements. NTD Data Tables organize and summarize data from the 2022 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Way Mileage database files. In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the data tables from before 2015, you can find corresponding data in the file called "Transit Way Mileage - Rail Modes" and "Transit Way Mileage - Non-Rail Modes." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- 2017 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- The motor vehicle registration dashboard shows the number and type of vehicle (automobile, truck, motorcycle, and bus) registered over time in each state.1last year
- The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. Basic Safety Messages (BSM) sent by connected vehicles (CVs) through either the cellular network or Dedicated Short Range Communication (DSRC) when the vehicle is in the range of Roadside Units (RSU). These messages were received by the traffic management center (TMC).1last year
- The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the National Center for Environmental Information – National Oceanic and Atmospheric Administration. Precipitation information from this data source is used in the cluster analysis.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- Contains all PDCMs generated during the AMCD field testing program. The PDCM is a control message sent from the server to OBUs to customize a request for Probe Vehicle Data (PVD) from the receiving OBU. All PDCMs are generated by the VCC Cloud server and transmitted to OBU clients through either a DSRC or cellular communications channel.1last year
- Historic Highway Performance Monitoring System data sample for the year 19971last year
- This dataset reports the historical National Highway System 50th percentile median speeds for various roadway types, months, and vehicles on US roads.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains queue warning messages that were recommended by the INFLO Q-WARN algorithm and sent by the traffic management center to vehicles to warn drivers upstream of the queue. The objective of queue warning is to provide a vehicle operator sufficient warning of impending queue backup in order to brake safely, change lanes, or modify route such that secondary collisions can be minimized or even eliminated.1last year
- This dataset details federal funding sources for each applicable agency reporting to the NTD in the 2022 and 2023 report years. Federal funding sources are financial assistance obtained from the Federal Government to assist with the costs of providing transit services. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- About the Data The dataset includes publicly available NHTSA investigation information related to the identification and correction of safety-related defects in motor vehicles and vehicle equipment. For more information on NHTSA investigations, including safety defect investigations, please visit https://www.nhtsa.gov/resources-investigations-recalls.1last year
- 2016 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.1last year
- This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.1last year
- The data attached and/or displayed were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. A BSM is one of the messages belonging to the Society of Automotive Engineers (SAE) J2735 Standard. This standard is geared toward supporting the interoperability of DSRC applications through the use of a standardized message set and its data frames and data elements. A BSM, which is at times referred to as a “heartbeat” message, is a frequently transmitted message (usually at approximately 10Hz) that is meant to increase a vehicle’s situational awareness. These messages are intended to be used for a variety of applications to exchange safety data regarding a vehicle’s state. A typical BSM contains up to two parts. Part I, the binary large object (blob), is included in every BSM. It contains the fundamental data elements that describe a vehicle’s position (latitude, longitude, elevation) and motion (heading, speed, acceleration). Part II of a BSM contains optional data that is transmitted when required or in response to an event. Typically Part II contains data that serves as an extension of vehicle safety information (path history, path prediction, event flags) and data pertaining to the status of a vehicle’s components, such as lights, wipers, and brakes. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Messages such as MAP, Detectors, and Simulation results1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the Caltrans – Performance Measurement System (PeMS). Speed data from this dataset were used to derive the freeway travel time. There are three separate text files with one for each operational condition.1last year
- Massachusetts Department of Transportation (MassDOT) Work Zone Data Exchange (WZDx) v2.0 Feed SampleThis dataset provides information on work zones in the state of Massachusetts in a tabular format and is updated daily based on the live MassDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the MassDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. The live feed is currently compliant with the WZDx Specification v2.0.1last year
- This dataset details operating expenses for each applicable agency, mode, and type of service (TOS), split by expense type reporting to the National Transit Database in the 2022 and 2023 report years. Expense types include Vehicle Operations, General Administration, and more. Only Full Reporters report expenses by function and type. Expenses from other reporter types are included under Reduced Reporter Expenses. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Operating Expenses database files. In years 2015-2021, you can find this data in the "Operating Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- This dataset details capital expenses by capital use type (existing or expansion) for each applicable agency, mode, and type of service (TOS) reporting to the National Transit Database in the 2022 and 2023 report years. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Capital Use database files. In years 2015-2021, you can find this data in the "Capital Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details operating expenses for each applicable agency, mode, and type of service (TOS), split by expense type or "Object Class" reporting to the National Transit Database in the 2022 and 2023 report years.. Object classes include salaries and wages, fuel, and others. Only Full Reporters report expenses by function and type. Expenses from other reporter types are included under Reduced Reporter Expenses. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Operating Expenses database files. In years 2015-2021, you can find this data in the "Operating Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years, split by fund expenditure type: capital and operating. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The attached file has trip level information for Health Connector rides taken from October 1, 2024 to January 31, 2025. Any information that could identify the rider has been stripped. Each row corresponds to one ride request.1last year
- Intelligent Network Flow Optimization Prototype Infrastructure Traffic Sensor System Data AggregatorData is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains real-time volume, speed and loop occupancy data that were collected from WSDOT’s simulated roadway sensors every 20 seconds and aggregated according to user defined procedures and threshold by the Infrastructure Traffic Sensor System (TSS) Data Aggregator software.1last year
- Historic Highway Performance Monitoring System sample data for the year 20021last year
- The Federal Highway Administration (FHWA) has been receiving Highway inventory, usage, condition and performance data from State Departments of Transportation (DOT) since 1978 to support the program mission of the FHWA. Specifically, HPMS consists of detailed road segment data (63 Attributes) for higher order systems. Sample attributes for collector systems and summary data for the local roads. New requirements for HPMS took effect in 2014 that required each State DOTs to expand their Linear Referencing Systems (LRS), a statewide geospatial representation of their road system that includes all public roads. This requirement was put in place to support highway safety. States DOTs submit HPMS data annually to the FHWA following a prescribed format outlined in the Highway Performance Monitoring System Field Manual.1last year
- State DOTs will provide Local and Rural Minor Collector Mileage summarized by county, ownership, and Paved and Unpaved. This data is provided in a direct input by the State DOTs.1last year
- This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program.The freeway data consists of two months of data (Sept 15 2011 through Nov 15 2011) from dual-loop detectors deployed in the main line and on-ramps of a Portland-area freeway. The section of I-205 NB covered by this test data set is 10.09 miles long and the section of I-205 SB covered by this test data set is 12.01 miles long The data includes: flow, occupancy, and speed.1last year
- This a list of active and inactive railroads, companies, and other organizations related to railroad operations. Organization Type ID = 1 designates a railroad; 4 designates a non-railroad organization (e.g. company, shipper, public entity, etc.). If a code has a blank EndDate, this means the organization is active; a populated EndDate field means the organization is no longer active.1last year
- Motor Vehicle Registration Data by Energy Source :2016 -Present Vehicle types are compatible with FHWA Highway Statistics VM-1 "Total" counts of vehicles for a year are compatible with FHWA Highway Statistics MV-1 minus "Motorcycle." Motorcycle data are not included.1last year
- last year
- last year
- This dataset details stations for each agency and mode for stations reported to the National Transit Database in report years 2022 and 2023. These data include the type of facility and the decade in which it was built. In many cases, stations are reported by each mode and type of service that uses them. For example, a single station used by bus - directly operated, bus - purchased transportation, and commuter bus - directly operated would be reported three times. For more detail, please see the NTD Policy Manual. Rural reporters do not report passenger stations and are not included in this file. Modes Demand Response, Demand Response - Taxi, Vanpool, and Publico also do not report stations and are also excluded. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Facility Inventory database files. In years 2015-2021, you can find this data in the "Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Annual vehicle miles of travel by functional system for each of the 50 states, DC, and Puerto Rico from the Highway Statistics table VM-2. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)1last year
- last year
- This file includes event data reported to the National Transit Database (NTD) for Commuter Rail (CR) and Alaska Railroad (AR) modes, as well as Heavy Rail (HR) service reported for Port Authority Trans Hudson (NTD ID: 20098), Hybrid Rail (YR) service for the Tri-County Metropolitan Transportation District of Oregon (NTD ID: 00008), Hybrid Rail (YR) service for Denton County Transportation Authority (NTD ID: 60101), and Hybrid Rail (YR) service for Capital Metropolitan Transportation Authority (NTD ID: 60048). Because these services fall under the safety oversight of the Federal Railroad Administration, the agencies are not required to report Safety Events (e.g., collisions, derailments, etc.) to the Federal Transit Administration through the NTD. Security events occurring on transit-owned property for these entities are reported to NTD, but excluded from other files to preserve the integrity of those datasets. They are presented in this file for completeness and should be considered by any user attempting to understand the scope and scale of reportable Security Events reported by public transit operators.1last year
- last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- last year
- Contains metrics describing service consumption and service cost for each public transportation agency, by mode and type of service.1last year
- last year
- This dataset details maintenance facility capacities and counts for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Please note that because Rural Reporters are not required to report facility size counts, for these reporters null values appear under facility size columns, yet non-zero values may appear under Total Facilities. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Facilities database files. In years 2015-2021, you can find this data in the "Maintenance Facilities" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- This dataset details mechanical failures for each applicable agency, mode, and type of service (TOS) reporting to the National Transit Database in the 2022 and 2023 report years. Only Full Reporters report breakdowns. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Vehicle Maintenance database files. In years 2015-2021, you can find this data in the "Breakdowns" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.1last year
- Contains all Basic Mobility Messages (BMMs) collected during the Advanced Messaging Concept Development (AMCD) field testing program. While there is no specific standard in existence that addresses the content of a BMM, the descriptive definitions of the variables were derived from the J2735 standard where applicable. All BMMs are generated by OBUs and ultimately received by the VCC Cloud server.1last year
- FRA develop a spatial point layer of the rail bridges over road and water. The bridges are a snapshot and is not an offical or complete inventory of all bridges. Railroads change ownership, railroads are abandoned, bridges are replaced, etc. therefore it cannot be relied upon as being accurate.1last year
- FRA develop a spatial point layer of the rail bridges over road and water. The bridges are a snapshot and is not an offical or complete inventory of all bridges. Railroads change ownership, railroads are abandoned, bridges are replaced, etc. therefore it cannot be relied upon as being accurate.1last year
- Data represent the performance of prototype cooperative automated driving system applications for improving traffic mobility. The applications include the integrated highway prototype that consists of vehicle platooning, speed harmonization, and automated lane change and merge.1last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The GPS data set catalogs the vehicle operation data of the test vehicles that used for the MMITSS field testing. The data contains the performance and operation details of vehicles. This file contains a number of fields detailing elements such as vehicle position and speed, fidelity measures of GPS-based data elements, and vehicle operation data. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message1last year
- The main dataset is a 130 MB file of trajectory data (I90_94_moving_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for four distinct data collection “Runs” (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each “Run” (I-90-moving-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94moving.csv” for more details). The dataset defines six northbound lanes using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided: I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X. I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes. Annotation on Regions.zip, which includes images that visually map lanes (I90_91last year
- This dataset consists of truck size and weight enforcement data including number of trucks weighed, number of violations, and number of oversize/overweight permits, as reported by the States in their annual certification to FHWA.1last year
- This dataset details passenger eligibility and requirements for Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Counties and Places: https://data.transportation.gov/Public-Transit/Demand-Response-Geographic-Area-Coverage-Counties-/3kum-6vpd/about_data Service Schedules: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Service-S/4p55-emkp/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2VMCT-1 Verify V2V communication of BSMs vehicle 1 data1last year
- last year
- The data in this repository were collected from the San Diego, California testbed, namely, I-15 from the interchange with SR-78 in the north to the interchange with SR-163 in the south, along the mainline and at the entrance ramps. This file contains information on the field observation and simulation results for speed profile from the Dallas, Texas testbed. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.1last year
- This dataset contains a one-month sample of flattened EVENT data records from the New York City (NYC) Connected Vehicle (CV) Pilot that have undergone obfuscation of precise time and location details as well as other vehicle identifiers. The full unflattened event data from NYC CV pilot can be found in the ITS Sandbox. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2VMCT-1 Verify V2V communication of BSMs vehicle 2 data1last year
- Annual number of licensed drivers by sex and age groups from FHWA Highway Statistics table DL-220.1last year
- This data represents HPMS Sample limits that correspond to the HPMS Section Data. This dataset contains expansion factors that are used to expand the attributes to State wide aggregation. More information regarding the Sample dataset is contained in the HPMS Field Manual. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin1last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The Vehicle Trajectories file is populated with basic safety messages received from equipped vehicle within the communication range of an Roadside Equipment (RSEs). The data also contains elements that communicate additional details about the vehicle that is used for vehicle safety applications, and elements that communicate specific items of a vehicle‘s status that are used in data event snapshots which are gathered and periodically reported to an RSEs. These data are transmitted at a rate of 10 Hz. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message1last year
- last year
- last year
- GPS pings collected by study participants who rode conventional and e-bikes at Minute Man National Historic Park between April and September 2022.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin.1last year
- last year
- Contains ratios describing service and cost for each agency, mode, and type of service.1last year
- This is the EVENT data captured from the New York City CV Pilot project that was processed by the independent evaluators at Volpe. Additional data collected and data dictionary are in the attachments. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.1last year
- last year
- The main, combined file that is used for the 4 Views for each type: Departures, Freight, Seats, and Passengers. This combined dataset will not be published, but the 4 views will be published separately.1last year
- This dataset contains data on transit agency employees as reported to the National Transit Database in the 2022 and 2023 report years. It is organized by agency, mode, type of service, and Employee Type (Full Time or Part Time Employee). The NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis This dataset is based on the 2022 and 2023 Employees database files, which are published to the NTD at https://transit.dot.gov/ntd/ntd-data. Only Full Reporters report data on employees, and only for Directly Operated modes. Other reporter types, and Purchased Transportation service, do not appear in this file.1last year
- Data collected on the SS-30 form. Transit agencies report to the NTD security personnel in terms of Full-Time Equivalents (FTE) according to the staffing levels at the beginning of the year. One FTE typically works 40 hours per week. An agency may use any reasonable method to allocate personnel across modes, such as allocating based on modal ridership or on modal annual trips. In certain instances, agencies may base personnel numbers on the prior year’s total hours worked.1last year
- This dataset details fuel mileage and gallons/kilowatt hours for each agency, mode, and type of service (TOS) as reported by agencies submitted data to the National Transit Database (NTD) for the 2022 and 2023 report years. This file is based on the 2022 and 2023 Energy Consumption database files available at https://transit.dot.gov/ntd/ntd-data Data Tables organize and summarize data from the 2022 and 2023 NTD in a manner that is more useful for quick reference and summary analysis. Only Full Reporters report energy consumption. Other reporter types do not appear in this dataset. Demand Response Taxi (DR/TX) mode and type of service combination does not report energy consumption and does not appear in this dataset. Finally, Non-dedicated fleets report energy consumption but not miles traveled. Thus for some agencies the given data for miles traveled are incomplete. Non-dedicated fleets represent about 7% of the data reflected in this dataset. In versions of the data tables from 2014-2021, you can find data on fuel and energy in the file called "Fuel and Energy" available from the NTD program website.1last year
- dataset of oceangoing, self-propelled, privately-owned U.S.-flag vessels of 1,000 gross tons and above that carry cargo from port to port for commercial and government customers.1last year
- Contains all Basic Mobility Control Message (BMCMs) generated during the Advanced Messaging Concept Development (AMCD) field testing program. While there is no specific standard in existence that addresses the content of a BMCM, the following format was derived to control the configuration and content of BMMs requested from the vehicle. All BMCMs are generated by the VCC Cloud server and transmitted to OBU clients through either a DSRC or cellular communications channel.1last year
- last year
- This dataset includes the inputs and results for developing a transportation geo-typology that categorizes every location in the United States in terms of their main drivers of transportation demand and supply. It provides the raw inputs to the census tract level microtypes and county or CBSA level geotypes as well as the final typology labels at both the tract (microtype) and county/CBSA (geotype) levels. Inputs include information on the street network, economic characteristics, topography, commute patterns, and land use. The methodology is published in "Popovich, N., Spurlock, C. A., Needell, Z., Jin, L., Wenzel, T., Sheppard, C., & Asudegi, M. (2021). A methodology to develop a geospatial transportation typology. Journal of transport geography, 93, 103061".1last year
- The FRA Milepost is a spatial file that originates of multiple sources and contains point locations of mileposts along the FRA's rail network. The mileposts was developed from varies sources and should only be used as a reference file. The railroad lines and their mileposts are privately owned and are subjected of changed based on the rail owner. If used for identifying specific locations, please contact the railroad to verify the mileposts numbers and their locations.1last year
- last year
- last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2IMCT-1 Verify V2I communication for log file offload.1last year
- 1last year
- This dataset offers insight on weekly fluctuation of the gasoline product supply, which is an important part of any analysis of construction trends, materials and operating costs associated with highway repair and construction, and changes in traffic volume. These data come directly from the Energy Information Administration (EIA) website. The EIA publishes the average daily amount of gasoline supplied in barrels, which HPPI converts to an average number of gallons of gasoline per week.1last year
- last year
- Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).1last year
- This dataset details places and counties served by Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Passenger Eligibility and Requirements: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Passenger/h9qc-expu/about_data Service Schedules: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Service-S/4p55-emkp/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The FMCSA Safety Measurement System (SMS) data, consists of active Intrastate Non-Hazmat Motor Carriers of property and/or passengers. File is comma delimited. One carrier per row.1last year
- This dataset contains a sample of the broadcast Traveler Information Messages (TIM) being generated by the Wyoming Connected Vehicle (CV) Pilot. The full set of TIMs can be found in the ITS DataHub data sandbox. Revision Note: This dataset only contains TIM sample data prior to December 18, 2018. For the most recent sample of TIM data, please refer to the Schema Version 6 dataset or retrieve the data from the ITS DataHub data sandbox.1last year
- Historic Highway Performance Monitoring System sample data for the year 20031last year
- last year
- Curated FRA Safety data pertaining to Rail Equipment Accidents (Form 54) Unique Train Accidents Please note that this dataset displays unique train accidents. When an accident involves multiple railroads, each railroad must report its data. As a result, there can be multiple records for one accident. This dataset has been modified to pull and display one record for each accident. Highway-rail crossing incidents have also been removed from this dataset because they are not considered train accidents. To see the full dataset with all reports with all data for all accidents, please visit https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj1last year
- last year
- The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).1last year
- State, County and City FIPS (Federal Information Processing Standards) codes are a set of numeric designations given to state, cities and counties by the U.S. federal government. All geographic data submitted to the FRA must have a FIPS code.1last year
- 2015 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- *Dataset* Records showing the history of each authority granted to a carrier/broker/freight forwarder, along with the dates of the original authority action (e.g., “granted”) and the final authority action (e.g., “revoked”). The dataset contains the DOT number and docket number of the entity that holds or held the authority. As there can be multiple authorities for a single entity, there may be multiple records for an entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- *Dataset* Information on the implementation dates of an active or pending insurance policy (posted date, effective date and cancel effective date). In addition to these dates, the record contains the insurance company name, the BI&PD underlying limit and maximum limit amounts, and the DOT number and docket number of the carrier/broker/freight forwarder that holds the policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- *Dataset* Records for carrier/broker/freight forwarder active or pending individual insurance policies. The records are linked to the entities by docket numbers included in the dataset. The dataset contains information on the insurance policy, including insurance company name, policy number and type of insurance. Entities can hold multiple insurance policies, so there may be multiple records associated with a particular entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- *Dataset* Information on insurance forms that were rejected by FMCSA. The dataset contains information on the insurance policy associated with the form, along with the date that the form was rejected and the reason for rejection (e.g., “Policy is already cancelled”). The dataset contains the DOT number and docket number of the carrier/broker/freight forwarder associated with the policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- The National Bicycle Network is a geospatial dataset for nationwide bicycle routes. It is based on data and information released by public agencies such as state transportation departments, local Metropolitan Planning Organizations, local Councils of Government, city, and county public works and transportation departments. The FHWA Office of Highway Policy Information (HPPI) integrates all releases into one nationwide bicycle network, construction, and operating of such facilities as a safe, efficient, and equitable travel mode.1last year
- Test Data of Proof-of-Concept Vehicle Platooning Based on Cooperative Adaptive Cruise Control (CACC)The data represent the performance of a proof-of-concept vehicle platooning based on the Cooperative Adaptive Cruise Control (CACC) application. The Federal Highway Administration’s Turner Fairbank Highway Research Center (TFHRC), in conjunction with the Volpe National Transportation Systems Center, tested and evaluated this prototype system in 2016. Researchers in the Saxton Transportation Operations Laboratory at TFHRC designed and built the Cooperative Automated Research Mobility Applications (CARMA) platform version 1 that enables the implementation of the proof-of-concept CACC-based platooning in passenger vehicles equipped with production adaptive cruise control, and vehicle-to-vehicle communications using dedicated short-range communications (DSRC). The data characterize the state-of-the-art capability of the CACC application based on engineering tests that were performed on closed tracks by professional drivers and using prescribed test procedures. The test data are separated into sets that correspond to test date and time, and test run number. The data include performance parameters that were collected from the CACC application and data acquisition systems, including vehicle controller area network data, CARMA's MicroAutoBox, DSRC radios, and an independent measurement system. The tests were conducted at US Army’s Aberdeen Test Center located at Aberdeen Proving Grounds, MD. Further documentation can be found here: https://rosap.ntl.bts.gov/view/dot/1038.1last year
- This dataset details vehicle types and ages for each transit agency reporting to the NTD in the 2022 and 2023 report years. Non-dedicated fleets do not report Year of Manufacture and are thus excluded from the Age Distribution table. Agencies do not report Useful Life Benchmark for non-dedicated fleets or fleets for which the agency does not have capital replacement responsibility. These fleets are excluded from calculations of the percentage of vehicles meeting or exceeding their useful life. In versions of the data tables from before 2014, you can find data on vehicles in the file called "Age Distribution of Active Vehicle Inventory." In years 2014-2021, you can find this data in the "Vehicles" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details service and cost efficiency metrics for agencies reporting to the National Transit Database in the 2022 and 2023 report years. Only Full Reporters report data on Passenger Miles. The columns containing ratios have been calculated as the average across all reporting modes, not as the ratio of summed data. Thus, each transit agency received equal weight, regardless of that agency's total ridership. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Federal Funding Allocation, Operating Expenses, and Service database files. In years 2015-2021, you can find this data in the "Metrics" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the NTD data tables from before 2014, you can find data on metrics in the files called "Fare per Passenger and Recovery Ratio" and "Service Supplied and Consumed Ratios." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- Beginning in 2023, certain agencies are required to submit one week of service data on a monthly basis to comply with FTA’s Weekly Reference reporting requirement on form WE-20. This data release will therefore present the limited set of key indicators reported by transit agencies on this form and will be updated each month with the most current data. The resulting dataset provides data users with data shortly after the transit service was provided and consumed, over one month in advance of FTA’s routine update to the Monthly Ridership Time Series dataset. One use of this data is for reference in understanding ridership patterns (e.g., to develop to a full month estimate ahead of when the data reflecting the given month of service is released by FTA at the end of the following month). Generally, FTA has defined the reference week to be the second or third full week of the month. All sampled agencies will report data referencing the same reference week. The form collects the following service data points, as described in the metadata below: • Weekday 5-day UPT total for the reference week; • Weekday 5-day VRM total for the reference week; • Weekend 2-day UPT total for either the weekend preceding or following the reference week; and • Weekend 2-day VRM total for either the weekend preceding or following the reference week. • Vehicles Operated in Maximum Service (vanpool mode only) for the reference week. FTA has also derived the change from the prior month for the same agency/mode/type of service/data point. Users should take caution when aggregating this measure and are encouraged to use the dataset export to measure service trends at a higher level (i.e., by reporter or nationally). For any questions regarding this dataset, please contact the NTD helpdesk at ntdhelp@dot.gov .1last year
- Historic Highway Performance Monitoring System sample data for the year 20041last year
- *Dataset* Records for each BOC3 agent hired by a carrier/broker/freight forwarder. Each entity must hire a BOC3 agent to represent them in legal matters to obtain operating authority. In some cases, entities may act as their own BOC3 agent. The records in the dataset contain the BOC3 agent’s name and address. The dataset also contains the DOT number and docket number of the represented entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- Identifies the study field and study results that arise from ad hoc examination of items, usually inspected in support of a particular study or verification/refutation of a specific trend. This inspection type is a Level IV inspection.1last year
- Historic Highway Performance Monitoring System sample data for the year 20061last year
- The FMCSA Safety Measurement System (SMS) data, consists of summary results of all active Interstate and Intrastate Hazmat Motor Carriers of passengers only. File is comma delimited.1last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Signal Phasing and Timing Message (SPaT) Messages transmitted by road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, SPaT data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow SAE J2735 data frames (Section 6) and structure (Section 7). This dataset holds a flattened sample of the SPaT data from Tampa CV Pilot. A column of random numbers (randomNum) was added to allow for random sampling of data points within Socrata.1last year
- last year
- This dataset details vehicle types and ages for transit agencies reporting to the National Transit Database in the 2022 and 2023 report years. Vehicle types describe the vehicles employed in direct operation or support of transit service. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Vehicle Inventory and Service Vehicle Inventory database files. Rural reporters that operate in more than one state report their vehicles in only one of their states. In years 2015-2021, you can find this data in the "Vehicles" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.1last year
- Contains all PVDs generated during the AMCD field testing program. The probe vehicle message is used to exchange status about a vehicle with other DSRC readers to allow the collection of information about a typical vehicle’s traveling behaviors along a segment of road. The exchanges of this message as well as the event which caused the collection of various elements defined in the messages are in Annex B of the SAE J2735 standard.1last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data1last year
- last year
- *Dataset* Information on carrier/broker/freight forwarder authorities that have been revoked by FMCSA. The dataset includes the DOT number and docket number of the entity, the type of authority revoked, and the reason. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- Historic Highway Performance Monitoring System sample data for the year 20091last year
- Part of the Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data1last year
- This dataset shows Amtrak stations in opportunity zones1last year
- This dataset details service schedules for Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Counties and Places: https://data.transportation.gov/Public-Transit/Demand-Response-Geographic-Area-Coverage-Counties-/3kum-6vpd/about_data Passenger Eligibility and Requirements: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Passenger/h9qc-expu/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Historic Highway Performance Monitoring System sample data for the year 20071last year
- Licensed driver data from Highway Statistics table DL-22, broken down by state, sex, and age group.1last year
- The FMCSA Safety Measurement System (SMS) data, consists of summary results of all active Interstate and Intrastate Hazmat Motor File Description: Carriers of property and/or passengers. File is comma delimited. One carrier per row.1last year
- This dataset contains a sample of the broadcast Traveler Information Messages (TIM) being generated by the Wyoming Connected Vehicle (CV) Pilot. This dataset only contains SchemaVersion 6 TIM sample data from December 18, 2018 to present. It is updated hourly and will hold up to 3 million of the most recent TIM records. The Schema Version 6 data is described further here. For sample TIM data prior to December 18, 2018, please refer to the Schema Version 5 dataset. The full set of TIMs can be found in the ITS Sandbox.1last year
- Provides detailed fare information for highest and lowest fare markets under 750 miles. For a more complete explanation, please read the introductory information at the beginning of Table 5 itself in the report (https://www.transportation.gov/office-policy/aviation-policy/domestic-airline-consumer-airfare-report-pdf).1last year
- Identifies the type, make, company number, license plate, license plate state, VIN, CVSA Decal, and CVSA Number. There can be multiple Inspection Units per inspection.1last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation.The Signal Plans for Roadside Equipment (RSE) data contains the basics of a Signal Phase and Timing (SPAT) message. This data includes SPAT message and the timestamp of the SPAT message. The data also provides the signal phase and timing information for one or more movements at an intersection.1last year
- Historic Highway Performance Monitoring System sample data for the year 20081last year
- The report includes inspections involving violations of the FMCSR or HRM.1last year
- The FMCSA New Entrant Safety Assurance Program out of service (OOS) data, consists of all entities that have received an OOS order from FMCSA. File is comma delimited.1last year
- This dataset details station/facility types and counts for each applicable agency reported to the National Transit Database for report years 2022 and 2023. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Facilities and Transit Stations database files. In years 2015-2021, you can find this data in the "Facilities and Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Traveler Information Messages (TIMs) transmitted by road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, TIM data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow a SAE J2735 TIM message structure to convey important traffic information to onboard units (OBU) of equipped vehicles. Refer to SAE J2735 Section 5.16 Message: MSG_TravelerInformation Message (TIM). This dataset holds a flattened sample of the TIM data from Tampa CV Pilot. Three additional fields were added to this Socrata dataset during ETL: a geo column (travelerdataframe_msgId_position) to allow for mapping of the geocoded TIM data within Socrata, a random number column (randomNum) to allow for random sampling of data points within Socrata, and a time of day generated column (metadata_generatedAt_timeOfDay) to allow for filtering of data by generated time.1last year
- last year
- last year
- The FMCSA Crash File contains data from state police crash reports involving drivers and vehicles of motor carriers operating in the U.S. Each report contains about 80 data elements pertaining to the motor carrier, driver, vehicles, and circumstances of a crash. Due to sensitive and/or privacy restrictions, driver, and hazardous materials data are not included in any crash files released to the public. The Crash File may contain multiple records for a crash. Separate reports are entered for each commercial motor vehicle involved in a crash. These multiple reports can be distinguished by the Crash Report Number field.1last year
- The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 3.0.1last year
- last year
- This data set shows Amtrak industrial, office, and commercial real estate in opportunity zones1last year
- The FMCSA Safety Measurement System (SMS) data, consists of active Intrastate Non-Hazmat Motor Carriers of passengers only. File is comma delimited. One carrier per row.1last year
- The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.1last year
- last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Highway-Rail-Grade-Crossing-Accident-Data/7wn6-i5b9.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form-55-Source-Table/unww-uhxd.1last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Basic Safety Messages (BSMs) generated by participant and public transportation vehicles onboard units (OBU) and transmitted to road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, BSM data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow SAE J2735 and J2945/1 standards and adopted units of measure. This dataset holds a flattened sample of the BSM data from Tampa CV Pilot. An extra geo column (coreData_position) was added to this dataset to allow for mapping of the geocoded BSM data within Socrata, and a column of random numbers (randomNum) was added to allow for random sampling of data points within Socrata.1last year
- *Dataset* This dataset contains information on a carrier’s/broker’s/freight forwarder’s previous insurance policy(ies). This dataset contains the DOT number and docket number of the entity. Additionally, it contains the cancellation method (cancelled, replaced, name change, transferred), the type of policy, the policy number, and the effective and cancellation dates of the policy. All insurance information is related to the insurance policy either being cancelled, being replaced, or prior to a name change. It is not the subsequent (if applicable) policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- The objective of this dataset is to create a location where there is a comprehensive list of all technologies, best practices and lessons learned from the Office of International Programs as a whole.1last year
- Massachusetts Department of Transportation (MassDOT) Work Zone Data Exchange (WZDx) v3.1 Feed SampleThis dataset provides information on work zones in the state of Massachusetts in a tabular format and is updated daily based on the live MassDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the MassDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. This live feed is currently compliant with the WZDx Specification v3.1.1last year
- Modal Service data and Safety & Security (S&S) public transit time series data delineated by transit/agency/mode/year/month. Includes all Full Reporters--transit agencies operating modes with more than 30 vehicles in maximum service--to the National Transit Database (NTD). This dataset will be updated monthly. The monthly ridership data is released one month after the month in which the service is provided. Records with null monthly service data reflect late reporting. The S&S statistics provided include both Major and Non-Major Events where applicable. Events occurring in the past three months are excluded from the corresponding monthly ridership rows in this dataset while they undergo validation. This dataset is the only NTD publication in which all Major and Non-Major S&S data are presented without any adjustment for historical continuity.1last year
- *Dataset* Records for all carriers/brokers/freight forwarders with active, inactive, or pending authorities (common or contract). It includes the DOT number and MC/FF/MX number for the carrier/broker/freight forwarder, along with company census data (e.g., types of authority, address, types of insurance on file, and amounts of insurance on file). See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data is a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Injury-Illness-Summary-Operational-Data/m8i6-zdsy.1last year
- Federal and State field enforcement staff performs Inspections on Interstate and Intrastate Motor Carriers and Hazardous Materials carriers. Violations of the Federal Motor Carrier Safety Regulations (FMCSRs) severe enough may result in a vehicle and/or driver being placed "out-of-service." The data collected from inspection activity is collected and stored in the FMCSA Motor Carrier Management Information System (MCMIS) Inspection Data Files. Due to privacy restrictions, driver information is not included in any inspection files released to the public.1last year
- The National Highway Construction Cost Index (NHCCI) is a price index that can be used both to track price changes associated with highway construction costs, and to convert current dollar expenditures on highway construction to real or constant dollar expenditures. This dataset contains the quarterly NHCCI estimates as well as the Seasonally Adjusted NHCCI and Component Contributions to Changes in NHCCI. Visit https://www.fhwa.dot.gov/policy/otps/nhcci/ for more information regarding the NHCCI.1last year
- This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases. Artificial intelligence (AI) promises to drive the growth of the United States economy and improve the quality of life of all Americans. Pursuant to Section 5 of Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government," Federal agencies are required to inventory their AI use cases and share their inventories with other government agencies and the public. In accordance with the requirements of EO 13960, this spreadsheet provides the mechanism for federal agencies to create their inaugural AI use case inventories. https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government1last year
- The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.1last year
- Counts of Non-Major Safety and Security Events are reported to the National Transit Database on a monthly basis, by transit agency and transit mode. These include minor fires on transit property requiring suppression, transit worker assaults not involving transport for medical attention, and other safety events that are not reportable as Major Events because a Major Event reporting threshold is not met (see Safety and Security Events dataset for a list of Major Events). In this file you will find the number of occurrences or safety incidents per month and the number of injuries in Safety Events (Safety/Security = SAF) where an individual was immediately transported away from the scene for medical attention due to those occurrences. There will be one entry for any transit mode/location with at least one occurrence for the given month. The file also contains Transit Worker Assaults which did not immediately transport away from the scene for 2023-present, as well as other Security Events (Safety/Security = SEC) reported historically but no longer collected by FTA. Note that an assault involving transport away from the scene for medical attention meets the Injury threshold and is not counted in this dataset. Agencies are not required to provide details for these events, and any description provided is omitted. The description can be available upon request. Update 5/6/24: FTA has updated its validation procedure for Non-Major S&S events to allow for inclusion in the data publication sooner in certain cases. This month, users of this dataset may notice a larger increase in S&S events than normal for certain records in 2023-2024 (only years for which data collection and validation is presently ongoing) compared to prior releases. This was done to allow for a more timely release of validated data.1last year
- Dataset containing all of the Federal Funding Allocation inputs submitted by reporting transit agencies to the National Transit Database in the 2022 and 2023 report years. This reflects the most recently published data within the Federal Transit Administration's NTD Data website.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Injury-Illness-Summary-Casualty-Data/rash-pd2d.1last year
- The Company Census File contains records for active entities registered with FMCSA. Active entities include those entities subject to the FMCSR, HMR, or intrastate non-Hazardous Material (HM) carriers. To identify each entity, FMCSA assigns a unique number to each entity record. This number is referred to as the USDOT number. Each Census record contains entity identifying data, business operations data, equipment and driver data, and carrier review data.1last year
- All Railroads covered by Part 225 Accident/Injury reporting are required to provide monthly summary statistics via the form F6180.55.1last year
- This represents the Service data reported to the NTD by transit agencies to the NTD. In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj.1last year
- Contains all Basic Safety Messages (BSMs) collected during the Advanced Messaging Concept Development (AMCD) field testing program. For this project, all of the Part I BSM message fields were populated. Additional data fields were also added to the row to identify sender, time of communication, mode of communication, etc., allowing the consumer of this data set to accurately track messages through the system. All BSMs are generated by OBUs and ultimately received by the VCC Cloud server.1last year
- Provides fare premiums for airports in the top 1,000 city pairs, and demonstrates the impact of low-fare service and hub domination on fare levels. All records are aggregated as directionless city pair markets. Air traffic in each direction is combined. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- Data summarized by city, includes the number of city-pair markets in the top 1,000 in either comparison period that involve each city, the number of passengers traveling to and from each city, the average fare, average fare per mile (yield), and average distance traveled. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/DownloadCrossingInventoryData.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Current/m2f8-22s6.1last year
- This is a list of all Major Safety and Security Events from January of 2014 to the most recently published data within the Federal Transit Administration's major event time series.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form57-Source-Table/icqf-xf4w.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form55a-Source-Table/kuvg-3uwp.1last year
- Tens of millions of vehicles with Takata air bags are under recall. Long-term exposure to high heat and humidity can cause these air bags to explode when deployed. Such explosions have caused injuries and deaths. NHTSA urges vehicle owners to take a few simple steps to protect themselves and others from this very serious threat to safety. This dataset tracks various progress indicators for the recall.1last year
- This dataset provides information on work zones in the state of North Carolina in a tabular format and is updated daily based on the live NCDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the NCDOT WZDx feed data can be found at the ITS WorkZone Raw Data Sandbox and the ITS Work Zone Semi-Processed Data Sandbox. The live feed is currently compliant with the WZDx Specification v3.1.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5).1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).1last year
- Annual motor vehicle registrations by vehicle type and state, from Highway Statistics table MV-1.1last year
- This dataset is in a user-friendly human-readable format. It contains the historical crossing inventory. To download the current inventory data, go here: https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Form-71-Current/m2f8-22s6.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).1last year
- This dataset is in a user-friendly human-readable format. It contains the current crossing inventory - one record for each crossing. To download historical data, go here: https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Historical/vhwz-raag. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Crossing-Inventory-Source-Data-Form-71-Current/xp92-5xme.1last year
- This dataset provides lane closure occurrences within the Texas Department of Transportation (TxDOT) highway system in a tabular format. A continuously updating archive of the TxDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. The live feed is currently compliant with the Work Zone Data Exchange (WZDx) Specification version 2.0.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr).1last year
- This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- This is list of data elements and their attributes that are used by data assets at the Federal Highway Administration.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form-54-Source-Table/aqxq-n5hy.1last year
- Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- This dataset contains the estimates of the vehicle miles traveled (VMT) for interstate highways and how the total travel measured by VMT compares with travel that occurred in the same week of the previous year.1last year
- Available on the internet only, this table is an expanded version of Table 1 that lists all city-pair markets in the contiguous United States that average at least 10 passengers each day. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- About the Data: The dataset includes recall information related to specific NHTSA campaigns. Users can filter based on characteristics like manufacturer and component. The dataset can also be filtered by recall type: tires, vehicles, car seats, and equipment. The earliest campaign data is from 1966. The dataset displays the completion rate from the latest Recall Quarterly Report or Annual Report data from Year 2015 Quarter 1 (2015-1) onward. Data Reporting Requirement: Manufacturers who determine that a product or piece of original equipment either contains a safety defect or is not in compliance with Federal safety standards are required to notify NHTSA within 5 business days. NHTSA requires that manufacturers file a Defect and Noncompliance Report in compliance with Federal Regulation 49 (the National Traffic and Motor Safety Act) Part 573, which identifies the requirements for safety recalls. This information is stored in the NHTSA database referenced above. Notes: The default visualization depicted here represents only the top 12 manufacturers for the current calendar year. Please use the Filters for specific data requests. For a complete historical perspective, please visit: https://www.nhtsa.gov/sites/nhtsa.gov/files/2023-03/2022-Recalls-Annual-Report_030223-tag.pdf.1last year
- Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf1last year
- Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.1last year
- Counts of Non-Major Safety and Security Events are reported to the National Transit Database on a monthly basis, by transit agency and transit mode. These include minor fires on transit property requiring suppression, transit worker assaults not involving transport for medical attention, and other safety events that are not reportable as Major Events because a Major Event reporting threshold is not met (see Safety and Security Events dataset for a list of Major Events). In this file you will find the number of occurrences or safety incidents per month and the number of injuries in Safety Events (Safety/Security = SAF) where an individual was immediately transported away from the scene for medical attention due to those occurrences. There will be one entry for any transit mode/location with at least one occurrence for the given month. The file also contains Transit Worker Assaults which did not immediately transport away from the scene for 2023-present, as well as other Security Events (Safety/Security = SEC) reported historically but no longer collected by FTA. Note that an assault involving transport away from the scene for medical attention meets the Injury threshold and is not counted in this dataset. Agencies are not required to provide details for these events, and any description provided is omitted. The description can be available upon request. Update 5/6/24: FTA has updated its validation procedure for Non-Major S&S events to allow for inclusion in the data publication sooner in certain cases. This month, users of this dataset may notice a larger increase in S&S events than normal for certain records in 2023-2024 (only years for which data collection and validation is presently ongoing) compared to prior releases. This was done to allow for a more timely release of validated data.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Injury-Illness-Summary-Casualty-Data/rash-pd2d.1last year
- All Railroads covered by Part 225 Accident/Injury reporting are required to provide monthly summary statistics via the form F6180.55.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj.1last year
- Provides fare premiums for airports in the top 1,000 city pairs, and demonstrates the impact of low-fare service and hub domination on fare levels. All records are aggregated as directionless city pair markets. Air traffic in each direction is combined. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/DownloadCrossingInventoryData.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Current/m2f8-22s6.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form57-Source-Table/icqf-xf4w.1last year
- Tens of millions of vehicles with Takata air bags are under recall. Long-term exposure to high heat and humidity can cause these air bags to explode when deployed. Such explosions have caused injuries and deaths. NHTSA urges vehicle owners to take a few simple steps to protect themselves and others from this very serious threat to safety. This dataset tracks various progress indicators for the recall.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5).1last year
- Annual motor vehicle registrations by vehicle type and state, from Highway Statistics table MV-1.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).1last year
- This dataset provides lane closure occurrences within the Texas Department of Transportation (TxDOT) highway system in a tabular format. A continuously updating archive of the TxDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. The live feed is currently compliant with the Work Zone Data Exchange (WZDx) Specification version 2.0.1last year
- This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.1last year
- This is list of data elements and their attributes that are used by data assets at the Federal Highway Administration.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form-54-Source-Table/aqxq-n5hy.1last year
- This dataset contains the estimates of the vehicle miles traveled (VMT) for interstate highways and how the total travel measured by VMT compares with travel that occurred in the same week of the previous year.1last year
- Massachusetts Department of Transportation (MassDOT) Work Zone Data Exchange (WZDx) v3.1 Feed SampleThis dataset provides information on work zones in the state of Massachusetts in a tabular format and is updated daily based on the live MassDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the MassDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. This live feed is currently compliant with the WZDx Specification v3.1.1last year
- *Dataset* Records for all carriers/brokers/freight forwarders with active, inactive, or pending authorities (common or contract). It includes the DOT number and MC/FF/MX number for the carrier/broker/freight forwarder, along with company census data (e.g., types of authority, address, types of insurance on file, and amounts of insurance on file). See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- Federal and State field enforcement staff performs Inspections on Interstate and Intrastate Motor Carriers and Hazardous Materials carriers. Violations of the Federal Motor Carrier Safety Regulations (FMCSRs) severe enough may result in a vehicle and/or driver being placed "out-of-service." The data collected from inspection activity is collected and stored in the FMCSA Motor Carrier Management Information System (MCMIS) Inspection Data Files. Due to privacy restrictions, driver information is not included in any inspection files released to the public.1last year
- This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases. Artificial intelligence (AI) promises to drive the growth of the United States economy and improve the quality of life of all Americans. Pursuant to Section 5 of Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government," Federal agencies are required to inventory their AI use cases and share their inventories with other government agencies and the public. In accordance with the requirements of EO 13960, this spreadsheet provides the mechanism for federal agencies to create their inaugural AI use case inventories. https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government1last year
- Modal Service data and Safety & Security (S&S) public transit time series data delineated by transit/agency/mode/year/month. Includes all Full Reporters--transit agencies operating modes with more than 30 vehicles in maximum service--to the National Transit Database (NTD). This dataset will be updated monthly. The monthly ridership data is released one month after the month in which the service is provided. Records with null monthly service data reflect late reporting. The S&S statistics provided include both Major and Non-Major Events where applicable. Events occurring in the past three months are excluded from the corresponding monthly ridership rows in this dataset while they undergo validation. This dataset is the only NTD publication in which all Major and Non-Major S&S data are presented without any adjustment for historical continuity.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data is a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Injury-Illness-Summary-Operational-Data/m8i6-zdsy.1last year
- The National Highway Construction Cost Index (NHCCI) is a price index that can be used both to track price changes associated with highway construction costs, and to convert current dollar expenditures on highway construction to real or constant dollar expenditures. This dataset contains the quarterly NHCCI estimates as well as the Seasonally Adjusted NHCCI and Component Contributions to Changes in NHCCI. Visit https://www.fhwa.dot.gov/policy/otps/nhcci/ for more information regarding the NHCCI.1last year
- The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.1last year
- Dataset containing all of the Federal Funding Allocation inputs submitted by reporting transit agencies to the National Transit Database in the 2022 and 2023 report years. This reflects the most recently published data within the Federal Transit Administration's NTD Data website.1last year
- The Company Census File contains records for active entities registered with FMCSA. Active entities include those entities subject to the FMCSR, HMR, or intrastate non-Hazardous Material (HM) carriers. To identify each entity, FMCSA assigns a unique number to each entity record. This number is referred to as the USDOT number. Each Census record contains entity identifying data, business operations data, equipment and driver data, and carrier review data.1last year
- This represents the Service data reported to the NTD by transit agencies to the NTD. In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Contains all Basic Safety Messages (BSMs) collected during the Advanced Messaging Concept Development (AMCD) field testing program. For this project, all of the Part I BSM message fields were populated. Additional data fields were also added to the row to identify sender, time of communication, mode of communication, etc., allowing the consumer of this data set to accurately track messages through the system. All BSMs are generated by OBUs and ultimately received by the VCC Cloud server.1last year
- Data summarized by city, includes the number of city-pair markets in the top 1,000 in either comparison period that involve each city, the number of passengers traveling to and from each city, the average fare, average fare per mile (yield), and average distance traveled. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- This is a list of all Major Safety and Security Events from January of 2014 to the most recently published data within the Federal Transit Administration's major event time series.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form55a-Source-Table/kuvg-3uwp.1last year
- This dataset provides information on work zones in the state of North Carolina in a tabular format and is updated daily based on the live NCDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the NCDOT WZDx feed data can be found at the ITS WorkZone Raw Data Sandbox and the ITS Work Zone Semi-Processed Data Sandbox. The live feed is currently compliant with the WZDx Specification v3.1.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).1last year
- This dataset is in a user-friendly human-readable format. It contains the historical crossing inventory. To download the current inventory data, go here: https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Form-71-Current/m2f8-22s6.1last year
- This dataset is in a user-friendly human-readable format. It contains the current crossing inventory - one record for each crossing. To download historical data, go here: https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Historical/vhwz-raag. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Crossing-Inventory-Source-Data-Form-71-Current/xp92-5xme.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr).1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- Available on the internet only, this table is an expanded version of Table 1 that lists all city-pair markets in the contiguous United States that average at least 10 passengers each day. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf1last year
- About the Data: The dataset includes recall information related to specific NHTSA campaigns. Users can filter based on characteristics like manufacturer and component. The dataset can also be filtered by recall type: tires, vehicles, car seats, and equipment. The earliest campaign data is from 1966. The dataset displays the completion rate from the latest Recall Quarterly Report or Annual Report data from Year 2015 Quarter 1 (2015-1) onward. Data Reporting Requirement: Manufacturers who determine that a product or piece of original equipment either contains a safety defect or is not in compliance with Federal safety standards are required to notify NHTSA within 5 business days. NHTSA requires that manufacturers file a Defect and Noncompliance Report in compliance with Federal Regulation 49 (the National Traffic and Motor Safety Act) Part 573, which identifies the requirements for safety recalls. This information is stored in the NHTSA database referenced above. Notes: The default visualization depicted here represents only the top 12 manufacturers for the current calendar year. Please use the Filters for specific data requests. For a complete historical perspective, please visit: https://www.nhtsa.gov/sites/nhtsa.gov/files/2023-03/2022-Recalls-Annual-Report_030223-tag.pdf.1last year
- Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data without rows where road_tmc and road are inconsistent.1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland 2019 Average Annual Daily Traffic data1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data without rows where road_tmc and road are inconsistent.1last year
- Annual length of U.S. public roads in miles by functional system for each of the 50 states, DC, and Puerto Rico from the Highway Statistics table HM-20. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is the processed integrated traffic data with work zone and incident information. Attached below are the number of lanes data and impacted work zone .pkl file.1last year
- Historic Highway Performance Monitoring System sample data for the year 19991last year
- Historic Highway Performance Monitoring System sample data for the year 19901last year
- Historic Highway Performance Monitoring System sample data for the year 19941last year
- Historic Highway Performance Monitoring System universe data for the year 19851last year
- Historic Highway Performance Monitoring System universe data for the year 19961last year
- Historic Highway Performance Monitoring System universe data for the year 20041last year
- Historic Highway Performance Monitoring System sample data for the year 19961last year
- Historic Highway Performance Monitoring System sample data for the year 19911last year
- Historic Highway Performance Monitoring System universe data for the year 19921last year
- Historic Highway Performance Monitoring System sample data for the year 19931last year
- Historic Highway Performance Monitoring System universe data for the year 20021last year
- Historic Highway Performance Monitoring System universe data for the year 19871last year
- Historic Highway Performance Monitoring System sample data for the year 19921last year
- Historic Highway Performance Monitoring System universe data for the year 19911last year
- Historic Highway Performance Monitoring System universe data for the year 20061last year
- Historic Highway Performance Monitoring System universe data for the year 19981last year
- Historic Highway Performance Monitoring System universe data for the year 20051last year
- Historic Highway Performance Monitoring System universe data for the year 19951last year
- Historic Highway Performance Monitoring System sample data for the year 19891last year
- Historic Highway Performance Monitoring System universe data for the year 19971last year
- Historic Highway Performance Monitoring System sample data for the year 19881last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is a raw sample of Maryland roadway speed data1last year
- Historic Highway Performance Monitoring System universe data for the year 19901last year
- Historic Highway Performance Monitoring System universe data for the year 20001last year
- Annual state reported motor vehicle registration data published in Highway Statistics table MV-1.1last year
- Historic Highway Performance Monitoring System universe data for the year 19861last year
- Historic Highway Performance Monitoring System universe data for the year 19931last year
- Historic Highway Performance Monitoring System sample data for the year 19801last year
- last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 20031last year
- Historic Highway Performance Monitoring System sample data for the year 19951last year
- Historic Highway Performance Monitoring System universe data for the year 20071last year
- last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 20001last year
- last year
- last year
- 1last year
- Historic Highway Performance Monitoring System sample data for the year 20011last year
- Historic Highway Performance Monitoring System universe data for the year 19941last year
- last year
- last year
- last year
- State DOT will provide VMT. This data is summarized by Paved and Unpaved and by Vehicle Type.1last year
- 1last year
- Historic Highway Performance Monitoring System universe data for the year 20011last year
- last year
- 1last year
- last year
- last year
- last year
- 1last year
- Historic Highway Performance Monitoring System universe data for the year 19891last year
- last year
- last year
- last year
- State DOT will provide Local Vehicle-Miles-Traveled (VMT) summarized by FHWA Adjusted Urban Area.1last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 19881last year
- last year
- last year
- State DOT will provide Local Vehicle-Miles-Traveled (VMT) summarized by FHWA Adjusted Urban Area.1last year
- Historic Highway Performance Monitoring System universe data for the year 20081last year
- Miles of public roads by the roadway's functional system and whether rural or urban for the 50 states, DC, and Puerto Rico (from 1996) from FHWA Highway Statistics table HM-60. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)1last year
- 1last year
- Test case WFCW-1 Results - FCW Stopped Vehicle Rep 21last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel.1last year
- State DOT will provide VMT. This data is summarized by Paved and Unpaved and by Vehicle Type.1last year
- This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.1last year
- The table displays the total number of licensed drivers in each State. The table shows the number of male and female licensed drivers by sex and age group.1last year
- Historic Highway Performance Monitoring System universe data for the year 19991last year
- WFCW-2 Stopped Vehicle Message Prioritization Rep 21last year
- Historic Highway Performance Monitoring System sample data for the year 19861last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The North contains data for the following States: Maine, New Hampshire, Vermont, New York, Massachusetts, Rhode Island, Connecticut, New Jersey, Pennsylvania, Ohio, Maryland, District of Columbia, and Delaware.1last year
- Historic Highway Performance Monitoring System data sample for the year 19981last year
- Historic Highway Performance Monitoring System universe data for the year 19831last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin.1last year
- State DOTs will provide Local and Rural Minor Collector Mileage summarized by county, ownership, and Paved and Unpaved. This data is provided in a direct input by the State DOTs.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The South contains data for the following States: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia, and Puerto Rico.1last year
- last year
- last year
- The Facility Inventory dataset details all facilities supporting public transit service as reported to the National Transit Database (NTD) by each public transit agency in the 2023 report year. This file is also published at https://www.transit.dot.gov/ntd/ntd-data, under the Product Type of "Annual Database (Excel)." Equivalent datasets from 2018 through 2022 can also be found using that link. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- 1last year
- last year
- The Summary Statistics dashboard includes rural and urban measures for roadway mileage, lane miles, vehicle miles traveled, fatalities, and fatality rate.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The South contains data for the following States: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia, and Puerto Rico.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The North contains data for the following States: Maine, New Hampshire, Vermont, New York, Massachusetts, Rhode Island, Connecticut, New Jersey, Pennsylvania, Ohio, Maryland, District of Columbia, and Delaware.1last year
- last year
- This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.1last year
- Summary monthly motor fuel data on the amount of on-highway fuel used at the national level. Includes the amount of gallons of gasoline/gasohol and special fuel (primarily diesel) taxed each month.1last year
- Summary monthly motor fuel data on the amount of on-highway fuel used at the state level. Includes the amount of gallons of gasoline/gasohol and special fuel (primarily diesel) taxed each month.1last year
- Historic Highway Performance Monitoring System universe data for the year 19801last year
- The main dataset is a 70 MB file of trajectory data (I294_L1_final.csv) that contains position, speed, and acceleration data for small and large automated (L1) vehicles and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for ten distinct data collection “Runs” (I294_L1_RunX_with_lanes.png, where X equals 8, 18, and 20 for southbound runs and 1, 3, 7, 9, 11, 19, and 21 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L1-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L1.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L1_Lane-2.png through I294_L1_Lane-5.png and the northbound lanes are shown visually in I294_L1_Lane2.png through I294_L1_Lane5.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day. As part of this dataset, the following files were provided: I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X. I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane cent1last year
- The main dataset is a 350 MB file of trajectory data (TGSIM-Foggy Bottom-Data.csv) that contains position, speed, and acceleration data for pedestrians, bicycles, scooters, non-automated passenger cars, automated vehicles, motorcycles, buses, and trucks in an urban environment. Supporting files include an aerial reference image (Reference_Image_Foggy Bottom.png) and a list of polygon boundaries (Foggy_Bottom_boundaries.txt) and associated images (i1.png, i2.png, …, i49.png stored in the folder titled “Annotation on Regions.zip”) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset. As part of this dataset, the following files were provided: TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.0186613838586-meter conversion. Reference_Image_Foggy Bottom.png is the aerial reference image that defines the geographic region and the associated roadway segments. Foggy_Bottom_boundaries.txt contains the coordinates that define the roadway segments (n = 49). Each polygon is a list of four to six coordinate pairs measured in pixels (which can be converted to meters using the provided 1 pixel = 0.0186613838586-meter conversion), with (0,0) global reference coordinates at the top-left of the reference image. Annotation on Regions.zip, which includes i1.png, i2.png,..., i49.png, are images that visually map the road segment IDs (indicated by the number following the i in the file name) to the reference image.1last year
- Historic Highway Performance Monitoring System sample data for the year 19831last year
- Counts of Non-Major Safety and Security Events are reported to the National Transit Database on a monthly basis, by transit agency and transit mode. These include minor fires on transit property requiring suppression, transit worker assaults not involving transport for medical attention, and other safety events that are not reportable as Major Events because a Major Event reporting threshold is not met (see Safety and Security Events dataset for a list of Major Events). This file includes event data reported to the National Transit Database (NTD) for Commuter Rail (CR) and Alaska Railroad (AR) modes, as well as Heavy Rail (HR) service reported for Port Authority Trans Hudson (NTD ID: 20098), Hybrid Rail (YR) service for the Tri-County Metropolitan Transportation District of Oregon (NTD ID: 00008), Hybrid Rail (YR) service for Denton County Transportation Authority (NTD ID: 60101), and Hybrid Rail (YR) service for Capital Metropolitan Transportation Authority (NTD ID: 60048). Because these services fall under the safety oversight of the Federal Railroad Administration, the agencies are not required to report Safety Events (e.g., collisions, derailments, etc.) to the Federal Transit Administration through the NTD. Security events occurring on transit-owned property for these entities are reported to NTD, but excluded from other files to preserve the integrity of those datasets. They are presented in this file for completeness and should be considered by any user attempting to understand the scope and scale of reportable Security Events reported by public transit operators.1last year
- The main dataset is a 232 MB file of trajectory data (I395-final.csv) that contains position, speed, and acceleration data for non-automated passenger cars, trucks, buses, and automated vehicles on an expressway within an urban environment. Supporting files include an aerial reference image (I395_ref_image.png) and a list of polygon boundaries (I395_boundaries.csv) and associated images (I395_lane-1, I395_lane-2, …, I395_lane-6) stored in a folder titled “Annotation on Regions.zip” to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns “x1” to “x5” represent the horizontal pixel values in the reference image, with “x1” being the leftmost boundary line and “x5” being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. The origin point of the reference image is located at the top left corner. The dataset defines five lanes with five boundaries. Lane -6 corresponds to the area to the left of “x1”. Lane -5 corresponds to the area between “x1” and “x2”, and so forth to the rightmost lane, which is defined by the area to the right of “x5” (Lane -2). Lane -1 refers to vehicles that go onto the shoulder of the merging lane (Lane -2), which are manually separated by watching the videos. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments. I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns "x1" to "x5" represent the horizontal pi1last year
- Historic Highway Performance Monitoring System sample data for the year 1980.1last year
- last year
- last year
- The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for six distinct data collection “Runs” (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each “Run” (I-90-stationary-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94Stationary.csv” for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X. I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and ve1last year
- Annual gallons of taxed motor fuel for the 50 states and DC from FHWA Highway Statistics table MF-202.1last year
- This dataset offers insight on weekly fluctuation of the gasoline product supply, which is an important part of any analysis of construction trends, materials and operating costs associated with highway repair and construction, and changes in traffic volume. These data come directly from the Energy Information Administration (EIA) website. The EIA publishes the average daily amount of gasoline supplied in barrels, which HPPI converts to an average number of gallons of gasoline per week.1last year
- This report contains tables and charts on the financial condition of the U.S. major airlines. All data presented in this financial and traffic review are derived from data reported to the U.S. Department of Transportation on Form 41 Schedules by Large Certificated Air Carriers. The data are presented on both a carrier group and an individual carrier basis, but the primary focus is on the individual major carrier and its performance. Data are presented for the most recent quarterly period and the comparable quarter a year earlier and also on a 12-month ended basis as at the end of the five most recent quarters. In addition, data on charges over comparable periods 12-months earlier are presented. A graphic presentation of comparative trends, on a carrier group basis, is made for several unit and overall financial indicators. In the case of merged carriers, data for the carriers involved have been combined and presented under the name of the surviving carrier so that meaningful comparisons could be made for the most recent 18 quarters. Also, carriers can move between groupings (Majors and Nationals) based on the criteria listed below over time. Each report includes 18 quarters of data. In the instance where a carrier falls into both groupings during the 18 quarters, a carrier will appear in both reports. The data from the Majors report and the data from the Nationals report should not be combined without ensuring any duplications are removed. Carrier Group Definitions Majors: Air carriers with annual operating revenues exceeding $1,000,000,000 Nationals: Air carriers with annual operating revenues between $100,000,000 and $1,000,000,000 Large Regionals: Air carriers with operating revenues between $20,000,000 and $99,000,000 Medium Regionals: Carriers with annual operating revenues less than $19,999,999 or that operate only aircraft with 60 seats or less (or 18,000 lbs maximum payload) https://www.transportation.gov/policy/aviation-policy/airline-quarterly-financial-review1last year
- Historic Highway Performance Monitoring System sample data for the year 19821last year
- Historic Highway Performance Monitoring System sample data for the year 19851last year
- last year
- The main dataset is a 9 MB file of trajectory data (I294_L2_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for twelve distinct data collection “Runs” (I294_L2_Run_X_ref_image_with_lanes.png, where X equals 5, 28, 30, 36, 38, and 42 for southbound runs and 23, 29, 31, 33, 35, and 41 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L2-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L2.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L2_lane-2.png through I294_L2_lane-5.png and the northbound lanes are shown visually in I294_L2_lane2.png through I294_L2_lane5.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided: I294_L2_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the L2 test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I294_L2_Run_X_ref_image_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X. I294_L2_Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. T1last year
- This a reference table for the Grade Crossing Inventory System, which is the application used to submit data for the Highway-Rail Grade Crossing Inventory (Form 71). The data dictionary for GCIS is attached as well. The LookupType column contains the name of the field/column in the source GCIS/Form 71 dataset. The LookupValue column contains the submitted value and the LookupText field is the human-readable text description of that value (e.g. for LookupType=TypeXing; LookupValue=3 and LookupText=Public, which designates that a crossing is public). This reference table can be used for the Crossing Inventory Source Data Form 71 – Current: https://datahub.transportation.gov/dataset/Crossing-Inventory-Source-Data-Form-71-Current/xp92-5xme.1last year
- Annual number of licensed drivers for the 50 states and DC from FHWA Highway Statistics table DF-201.1last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 19841last year
- 2019 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- last year
- This dataset details the ages of guideway elements for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Guideway elements include elements, structures, or facilities dedicated specifically to transit use, such as track, subway structures, tunnels, bridges, and propulsion power systems. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Way Mileage database files. In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the data tables from before 2015, you can find data on vehicles in the file called "Transit Way Mileage - Rail Modes." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- 2018 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- Historic Highway Performance Monitoring System sample data for the year 19801last year
- This dataset details funding from taxes levied by each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- As of Report Year (RY) 2023, FTA requires that reporters with fixed route modes create and maintain a public domain general transit feed specification (GTFS) dataset that reflects their fixed route service. This specification allows for the mapping and other geospatial data visualization and analyses of key transit elements such as stops, routes, and trips. At least one GTFS weblink is provided by the transit agency for each fixed route bus mode and type of service. These include all Rail modes as well as Bus, Bus Rapid Transit, Commuter Bus, Ferryboat and Trolleybus. GTFS requires that an overarching compressed file contain, at a minimum, seven underlying text files: (a) Agency; (b) Stops; (c) Routes; (d) Trips; (e) Stop Times; (f) Calendar or Calendar Dates.txt; and (g) Feed Info.txt. An eighth file, Shapes.txt, is an optional file. FTA collects and publishes these links for further analysis using related GTFS files. FTA is not responsible for managing the websites that host these files, and users with questions regarding the GTFS data are encouraged to contact the transit agency. In many cases, publicly hosted weblinks could not be provided (i.e., due to constraints within the transit agency), but the agency was able to produce a zip file of the required GTFS data. Demand Response, Vanpool, and other non-fixed route modes are excluded. The column "Alternate Format" indicates that the agency provided FTA a weblink in an alternate format with some justification for doing so. The file "Waived" indicates that no GTFS files were produced and FTA granted the agency a waiver from the requirement in Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2023 General Transit Feed Specification database file. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details state funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include General and Transportation funds. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In report year 2022, Extraordinary and Special Item Funds were reported under General Funds. In report year 2023, this was separated into its own category. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details directly generated funding for each agency. Examples include Fares, Concessions and Advertising. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Volume of taxed special fuel, primarily diesel, but including alternative fuels, reported by the States each month, based on reports from suppliers and distributors. These amounts are reported in various Office of Highway Policy Information (OHPI) products including the longstanding Monthly Motor Fuel Report, and the annual Highway Statistics publications.1last year
- last year
- 2020 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- This dataset details local funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Historic Highway Performance Monitoring System sample data for the year 20051last year
- The motor vehicle registration dashboard shows the number and type of vehicle (automobile, truck, motorcycle, and bus) registered over time in each state.1last year
- Volume of gasoline reported by the States each month, based on reports from suppliers and distributors. These amounts are reported in various Office of Highway Policy Information (OHPI) products including the longstanding Monthly Motor Fuel Report, and the annual Highway Statistics publications.1last year
- This dataset details track and roadway mileage/characteristics for each agency, mode, and type of service, as reported to the National Transit Database in Report Years 2022 and 2023. These data include the types of track/roadway elements employed in transit operation, as well as the length and/or count of certain elements. NTD Data Tables organize and summarize data from the 2022 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Way Mileage database files. In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the data tables from before 2015, you can find corresponding data in the file called "Transit Way Mileage - Rail Modes" and "Transit Way Mileage - Non-Rail Modes." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Select summary highway statistics, 1980 - 2017, mileage, lane-miles, vehicle miles traveled, and fatalities by state and functional system.1last year
- 2017 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. Basic Safety Messages (BSM) sent by connected vehicles (CVs) through either the cellular network or Dedicated Short Range Communication (DSRC) when the vehicle is in the range of Roadside Units (RSU). These messages were received by the traffic management center (TMC).1last year
- The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the National Center for Environmental Information – National Oceanic and Atmospheric Administration. Precipitation information from this data source is used in the cluster analysis.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- Contains all PDCMs generated during the AMCD field testing program. The PDCM is a control message sent from the server to OBUs to customize a request for Probe Vehicle Data (PVD) from the receiving OBU. All PDCMs are generated by the VCC Cloud server and transmitted to OBU clients through either a DSRC or cellular communications channel.1last year
- Historic Highway Performance Monitoring System data sample for the year 19971last year
- This dataset reports the historical National Highway System 50th percentile median speeds for various roadway types, months, and vehicles on US roads.1last year
- This dataset details federal funding sources for each applicable agency reporting to the NTD in the 2022 and 2023 report years. Federal funding sources are financial assistance obtained from the Federal Government to assist with the costs of providing transit services. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains queue warning messages that were recommended by the INFLO Q-WARN algorithm and sent by the traffic management center to vehicles to warn drivers upstream of the queue. The objective of queue warning is to provide a vehicle operator sufficient warning of impending queue backup in order to brake safely, change lanes, or modify route such that secondary collisions can be minimized or even eliminated.1last year
- About the Data The dataset includes publicly available NHTSA investigation information related to the identification and correction of safety-related defects in motor vehicles and vehicle equipment. For more information on NHTSA investigations, including safety defect investigations, please visit https://www.nhtsa.gov/resources-investigations-recalls.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.1last year
- 2016 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.1last year
- The data attached and/or displayed were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. A BSM is one of the messages belonging to the Society of Automotive Engineers (SAE) J2735 Standard. This standard is geared toward supporting the interoperability of DSRC applications through the use of a standardized message set and its data frames and data elements. A BSM, which is at times referred to as a “heartbeat” message, is a frequently transmitted message (usually at approximately 10Hz) that is meant to increase a vehicle’s situational awareness. These messages are intended to be used for a variety of applications to exchange safety data regarding a vehicle’s state. A typical BSM contains up to two parts. Part I, the binary large object (blob), is included in every BSM. It contains the fundamental data elements that describe a vehicle’s position (latitude, longitude, elevation) and motion (heading, speed, acceleration). Part II of a BSM contains optional data that is transmitted when required or in response to an event. Typically Part II contains data that serves as an extension of vehicle safety information (path history, path prediction, event flags) and data pertaining to the status of a vehicle’s components, such as lights, wipers, and brakes. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Messages such as MAP, Detectors, and Simulation results1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the Caltrans – Performance Measurement System (PeMS). Speed data from this dataset were used to derive the freeway travel time. There are three separate text files with one for each operational condition.1last year
- Massachusetts Department of Transportation (MassDOT) Work Zone Data Exchange (WZDx) v2.0 Feed SampleThis dataset provides information on work zones in the state of Massachusetts in a tabular format and is updated daily based on the live MassDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the MassDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. The live feed is currently compliant with the WZDx Specification v2.0.1last year
- This dataset details operating expenses for each applicable agency, mode, and type of service (TOS), split by expense type reporting to the National Transit Database in the 2022 and 2023 report years. Expense types include Vehicle Operations, General Administration, and more. Only Full Reporters report expenses by function and type. Expenses from other reporter types are included under Reduced Reporter Expenses. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Operating Expenses database files. In years 2015-2021, you can find this data in the "Operating Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- This dataset details capital expenses by capital use type (existing or expansion) for each applicable agency, mode, and type of service (TOS) reporting to the National Transit Database in the 2022 and 2023 report years. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Capital Use database files. In years 2015-2021, you can find this data in the "Capital Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details operating expenses for each applicable agency, mode, and type of service (TOS), split by expense type or "Object Class" reporting to the National Transit Database in the 2022 and 2023 report years.. Object classes include salaries and wages, fuel, and others. Only Full Reporters report expenses by function and type. Expenses from other reporter types are included under Reduced Reporter Expenses. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Operating Expenses database files. In years 2015-2021, you can find this data in the "Operating Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years, split by fund expenditure type: capital and operating. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The attached file has trip level information for Health Connector rides taken from October 1, 2024 to January 31, 2025. Any information that could identify the rider has been stripped. Each row corresponds to one ride request.1last year
- The Federal Highway Administration (FHWA) has been receiving Highway inventory, usage, condition and performance data from State Departments of Transportation (DOT) since 1978 to support the program mission of the FHWA. Specifically, HPMS consists of detailed road segment data (63 Attributes) for higher order systems. Sample attributes for collector systems and summary data for the local roads. New requirements for HPMS took effect in 2014 that required each State DOTs to expand their Linear Referencing Systems (LRS), a statewide geospatial representation of their road system that includes all public roads. This requirement was put in place to support highway safety. States DOTs submit HPMS data annually to the FHWA following a prescribed format outlined in the Highway Performance Monitoring System Field Manual.1last year
- Intelligent Network Flow Optimization Prototype Infrastructure Traffic Sensor System Data AggregatorData is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains real-time volume, speed and loop occupancy data that were collected from WSDOT’s simulated roadway sensors every 20 seconds and aggregated according to user defined procedures and threshold by the Infrastructure Traffic Sensor System (TSS) Data Aggregator software.1last year
- Historic Highway Performance Monitoring System sample data for the year 20021last year
- Annual vehicle miles of travel by functional system for each of the 50 states, DC, and Puerto Rico from the Highway Statistics table VM-2. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)1last year
- This a list of active and inactive railroads, companies, and other organizations related to railroad operations. Organization Type ID = 1 designates a railroad; 4 designates a non-railroad organization (e.g. company, shipper, public entity, etc.). If a code has a blank EndDate, this means the organization is active; a populated EndDate field means the organization is no longer active.1last year
- State DOTs will provide Local and Rural Minor Collector Mileage summarized by county, ownership, and Paved and Unpaved. This data is provided in a direct input by the State DOTs.1last year
- This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program.The freeway data consists of two months of data (Sept 15 2011 through Nov 15 2011) from dual-loop detectors deployed in the main line and on-ramps of a Portland-area freeway. The section of I-205 NB covered by this test data set is 10.09 miles long and the section of I-205 SB covered by this test data set is 12.01 miles long The data includes: flow, occupancy, and speed.1last year
- Motor Vehicle Registration Data by Energy Source :2016 -Present Vehicle types are compatible with FHWA Highway Statistics VM-1 "Total" counts of vehicles for a year are compatible with FHWA Highway Statistics MV-1 minus "Motorcycle." Motorcycle data are not included.1last year
- last year
- last year
- This dataset details stations for each agency and mode for stations reported to the National Transit Database in report years 2022 and 2023. These data include the type of facility and the decade in which it was built. In many cases, stations are reported by each mode and type of service that uses them. For example, a single station used by bus - directly operated, bus - purchased transportation, and commuter bus - directly operated would be reported three times. For more detail, please see the NTD Policy Manual. Rural reporters do not report passenger stations and are not included in this file. Modes Demand Response, Demand Response - Taxi, Vanpool, and Publico also do not report stations and are also excluded. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Facility Inventory database files. In years 2015-2021, you can find this data in the "Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- This file includes event data reported to the National Transit Database (NTD) for Commuter Rail (CR) and Alaska Railroad (AR) modes, as well as Heavy Rail (HR) service reported for Port Authority Trans Hudson (NTD ID: 20098), Hybrid Rail (YR) service for the Tri-County Metropolitan Transportation District of Oregon (NTD ID: 00008), Hybrid Rail (YR) service for Denton County Transportation Authority (NTD ID: 60101), and Hybrid Rail (YR) service for Capital Metropolitan Transportation Authority (NTD ID: 60048). Because these services fall under the safety oversight of the Federal Railroad Administration, the agencies are not required to report Safety Events (e.g., collisions, derailments, etc.) to the Federal Transit Administration through the NTD. Security events occurring on transit-owned property for these entities are reported to NTD, but excluded from other files to preserve the integrity of those datasets. They are presented in this file for completeness and should be considered by any user attempting to understand the scope and scale of reportable Security Events reported by public transit operators.1last year
- last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- last year
- Contains metrics describing service consumption and service cost for each public transportation agency, by mode and type of service.1last year
- last year
- This dataset details mechanical failures for each applicable agency, mode, and type of service (TOS) reporting to the National Transit Database in the 2022 and 2023 report years. Only Full Reporters report breakdowns. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Vehicle Maintenance database files. In years 2015-2021, you can find this data in the "Breakdowns" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details maintenance facility capacities and counts for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Please note that because Rural Reporters are not required to report facility size counts, for these reporters null values appear under facility size columns, yet non-zero values may appear under Total Facilities. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Facilities database files. In years 2015-2021, you can find this data in the "Maintenance Facilities" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.1last year
- FRA develop a spatial point layer of the rail bridges over road and water. The bridges are a snapshot and is not an offical or complete inventory of all bridges. Railroads change ownership, railroads are abandoned, bridges are replaced, etc. therefore it cannot be relied upon as being accurate.1last year
- Contains all Basic Mobility Messages (BMMs) collected during the Advanced Messaging Concept Development (AMCD) field testing program. While there is no specific standard in existence that addresses the content of a BMM, the descriptive definitions of the variables were derived from the J2735 standard where applicable. All BMMs are generated by OBUs and ultimately received by the VCC Cloud server.1last year
- FRA develop a spatial point layer of the rail bridges over road and water. The bridges are a snapshot and is not an offical or complete inventory of all bridges. Railroads change ownership, railroads are abandoned, bridges are replaced, etc. therefore it cannot be relied upon as being accurate.1last year
- Data represent the performance of prototype cooperative automated driving system applications for improving traffic mobility. The applications include the integrated highway prototype that consists of vehicle platooning, speed harmonization, and automated lane change and merge.1last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The GPS data set catalogs the vehicle operation data of the test vehicles that used for the MMITSS field testing. The data contains the performance and operation details of vehicles. This file contains a number of fields detailing elements such as vehicle position and speed, fidelity measures of GPS-based data elements, and vehicle operation data. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message1last year
- The main dataset is a 130 MB file of trajectory data (I90_94_moving_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for four distinct data collection “Runs” (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each “Run” (I-90-moving-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94moving.csv” for more details). The dataset defines six northbound lanes using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided: I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X. I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes. Annotation on Regions.zip, which includes images that visually map lanes (I90_91last year
- Annual number of licensed drivers by sex and age groups from FHWA Highway Statistics table DL-220.1last year
- This dataset details passenger eligibility and requirements for Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Counties and Places: https://data.transportation.gov/Public-Transit/Demand-Response-Geographic-Area-Coverage-Counties-/3kum-6vpd/about_data Service Schedules: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Service-S/4p55-emkp/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset consists of truck size and weight enforcement data including number of trucks weighed, number of violations, and number of oversize/overweight permits, as reported by the States in their annual certification to FHWA.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2VMCT-1 Verify V2V communication of BSMs vehicle 1 data1last year
- last year
- The data in this repository were collected from the San Diego, California testbed, namely, I-15 from the interchange with SR-78 in the north to the interchange with SR-163 in the south, along the mainline and at the entrance ramps. This file contains information on the field observation and simulation results for speed profile from the Dallas, Texas testbed. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.1last year
- This dataset contains a one-month sample of flattened EVENT data records from the New York City (NYC) Connected Vehicle (CV) Pilot that have undergone obfuscation of precise time and location details as well as other vehicle identifiers. The full unflattened event data from NYC CV pilot can be found in the ITS Sandbox. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2VMCT-1 Verify V2V communication of BSMs vehicle 2 data1last year
- This data represents HPMS Sample limits that correspond to the HPMS Section Data. This dataset contains expansion factors that are used to expand the attributes to State wide aggregation. More information regarding the Sample dataset is contained in the HPMS Field Manual. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin1last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The Vehicle Trajectories file is populated with basic safety messages received from equipped vehicle within the communication range of an Roadside Equipment (RSEs). The data also contains elements that communicate additional details about the vehicle that is used for vehicle safety applications, and elements that communicate specific items of a vehicle‘s status that are used in data event snapshots which are gathered and periodically reported to an RSEs. These data are transmitted at a rate of 10 Hz. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message1last year
- last year
- last year
- GPS pings collected by study participants who rode conventional and e-bikes at Minute Man National Historic Park between April and September 2022.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin.1last year
- last year
- This is the EVENT data captured from the New York City CV Pilot project that was processed by the independent evaluators at Volpe. Additional data collected and data dictionary are in the attachments. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.1last year
- Contains ratios describing service and cost for each agency, mode, and type of service.1last year
- The main, combined file that is used for the 4 Views for each type: Departures, Freight, Seats, and Passengers. This combined dataset will not be published, but the 4 views will be published separately.1last year
- This dataset contains data on transit agency employees as reported to the National Transit Database in the 2022 and 2023 report years. It is organized by agency, mode, type of service, and Employee Type (Full Time or Part Time Employee). The NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis This dataset is based on the 2022 and 2023 Employees database files, which are published to the NTD at https://transit.dot.gov/ntd/ntd-data. Only Full Reporters report data on employees, and only for Directly Operated modes. Other reporter types, and Purchased Transportation service, do not appear in this file.1last year
- last year
- Data collected on the SS-30 form. Transit agencies report to the NTD security personnel in terms of Full-Time Equivalents (FTE) according to the staffing levels at the beginning of the year. One FTE typically works 40 hours per week. An agency may use any reasonable method to allocate personnel across modes, such as allocating based on modal ridership or on modal annual trips. In certain instances, agencies may base personnel numbers on the prior year’s total hours worked.1last year
- This dataset details fuel mileage and gallons/kilowatt hours for each agency, mode, and type of service (TOS) as reported by agencies submitted data to the National Transit Database (NTD) for the 2022 and 2023 report years. This file is based on the 2022 and 2023 Energy Consumption database files available at https://transit.dot.gov/ntd/ntd-data Data Tables organize and summarize data from the 2022 and 2023 NTD in a manner that is more useful for quick reference and summary analysis. Only Full Reporters report energy consumption. Other reporter types do not appear in this dataset. Demand Response Taxi (DR/TX) mode and type of service combination does not report energy consumption and does not appear in this dataset. Finally, Non-dedicated fleets report energy consumption but not miles traveled. Thus for some agencies the given data for miles traveled are incomplete. Non-dedicated fleets represent about 7% of the data reflected in this dataset. In versions of the data tables from 2014-2021, you can find data on fuel and energy in the file called "Fuel and Energy" available from the NTD program website.1last year
- dataset of oceangoing, self-propelled, privately-owned U.S.-flag vessels of 1,000 gross tons and above that carry cargo from port to port for commercial and government customers.1last year
- Contains all Basic Mobility Control Message (BMCMs) generated during the Advanced Messaging Concept Development (AMCD) field testing program. While there is no specific standard in existence that addresses the content of a BMCM, the following format was derived to control the configuration and content of BMMs requested from the vehicle. All BMCMs are generated by the VCC Cloud server and transmitted to OBU clients through either a DSRC or cellular communications channel.1last year
- last year
- The FRA Milepost is a spatial file that originates of multiple sources and contains point locations of mileposts along the FRA's rail network. The mileposts was developed from varies sources and should only be used as a reference file. The railroad lines and their mileposts are privately owned and are subjected of changed based on the rail owner. If used for identifying specific locations, please contact the railroad to verify the mileposts numbers and their locations.1last year
- last year
- last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2IMCT-1 Verify V2I communication for log file offload.1last year
- 1last year
- This dataset offers insight on weekly fluctuation of the gasoline product supply, which is an important part of any analysis of construction trends, materials and operating costs associated with highway repair and construction, and changes in traffic volume. These data come directly from the Energy Information Administration (EIA) website. The EIA publishes the average daily amount of gasoline supplied in barrels, which HPPI converts to an average number of gallons of gasoline per week.1last year
- last year
- Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).1last year
- Historic Highway Performance Monitoring System sample data for the year 20061last year
- Contains all PVDs generated during the AMCD field testing program. The probe vehicle message is used to exchange status about a vehicle with other DSRC readers to allow the collection of information about a typical vehicle’s traveling behaviors along a segment of road. The exchanges of this message as well as the event which caused the collection of various elements defined in the messages are in Annex B of the SAE J2735 standard.1last year
- The FMCSA Safety Measurement System (SMS) data, consists of active Intrastate Non-Hazmat Motor Carriers of property and/or passengers. File is comma delimited. One carrier per row.1last year
- This dataset details places and counties served by Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Passenger Eligibility and Requirements: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Passenger/h9qc-expu/about_data Service Schedules: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Service-S/4p55-emkp/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset contains a sample of the broadcast Traveler Information Messages (TIM) being generated by the Wyoming Connected Vehicle (CV) Pilot. The full set of TIMs can be found in the ITS DataHub data sandbox. Revision Note: This dataset only contains TIM sample data prior to December 18, 2018. For the most recent sample of TIM data, please refer to the Schema Version 6 dataset or retrieve the data from the ITS DataHub data sandbox.1last year
- Historic Highway Performance Monitoring System sample data for the year 20031last year
- last year
- Curated FRA Safety data pertaining to Rail Equipment Accidents (Form 54) Unique Train Accidents Please note that this dataset displays unique train accidents. When an accident involves multiple railroads, each railroad must report its data. As a result, there can be multiple records for one accident. This dataset has been modified to pull and display one record for each accident. Highway-rail crossing incidents have also been removed from this dataset because they are not considered train accidents. To see the full dataset with all reports with all data for all accidents, please visit https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj1last year
- last year
- State, County and City FIPS (Federal Information Processing Standards) codes are a set of numeric designations given to state, cities and counties by the U.S. federal government. All geographic data submitted to the FRA must have a FIPS code.1last year
- The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).1last year
- 2015 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- *Dataset* Records showing the history of each authority granted to a carrier/broker/freight forwarder, along with the dates of the original authority action (e.g., “granted”) and the final authority action (e.g., “revoked”). The dataset contains the DOT number and docket number of the entity that holds or held the authority. As there can be multiple authorities for a single entity, there may be multiple records for an entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- *Dataset* Information on the implementation dates of an active or pending insurance policy (posted date, effective date and cancel effective date). In addition to these dates, the record contains the insurance company name, the BI&PD underlying limit and maximum limit amounts, and the DOT number and docket number of the carrier/broker/freight forwarder that holds the policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- *Dataset* Records for carrier/broker/freight forwarder active or pending individual insurance policies. The records are linked to the entities by docket numbers included in the dataset. The dataset contains information on the insurance policy, including insurance company name, policy number and type of insurance. Entities can hold multiple insurance policies, so there may be multiple records associated with a particular entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- *Dataset* Information on insurance forms that were rejected by FMCSA. The dataset contains information on the insurance policy associated with the form, along with the date that the form was rejected and the reason for rejection (e.g., “Policy is already cancelled”). The dataset contains the DOT number and docket number of the carrier/broker/freight forwarder associated with the policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- This dataset details vehicle types and ages for each transit agency reporting to the NTD in the 2022 and 2023 report years. Non-dedicated fleets do not report Year of Manufacture and are thus excluded from the Age Distribution table. Agencies do not report Useful Life Benchmark for non-dedicated fleets or fleets for which the agency does not have capital replacement responsibility. These fleets are excluded from calculations of the percentage of vehicles meeting or exceeding their useful life. In versions of the data tables from before 2014, you can find data on vehicles in the file called "Age Distribution of Active Vehicle Inventory." In years 2014-2021, you can find this data in the "Vehicles" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The National Bicycle Network is a geospatial dataset for nationwide bicycle routes. It is based on data and information released by public agencies such as state transportation departments, local Metropolitan Planning Organizations, local Councils of Government, city, and county public works and transportation departments. The FHWA Office of Highway Policy Information (HPPI) integrates all releases into one nationwide bicycle network, construction, and operating of such facilities as a safe, efficient, and equitable travel mode.1last year
- Test Data of Proof-of-Concept Vehicle Platooning Based on Cooperative Adaptive Cruise Control (CACC)The data represent the performance of a proof-of-concept vehicle platooning based on the Cooperative Adaptive Cruise Control (CACC) application. The Federal Highway Administration’s Turner Fairbank Highway Research Center (TFHRC), in conjunction with the Volpe National Transportation Systems Center, tested and evaluated this prototype system in 2016. Researchers in the Saxton Transportation Operations Laboratory at TFHRC designed and built the Cooperative Automated Research Mobility Applications (CARMA) platform version 1 that enables the implementation of the proof-of-concept CACC-based platooning in passenger vehicles equipped with production adaptive cruise control, and vehicle-to-vehicle communications using dedicated short-range communications (DSRC). The data characterize the state-of-the-art capability of the CACC application based on engineering tests that were performed on closed tracks by professional drivers and using prescribed test procedures. The test data are separated into sets that correspond to test date and time, and test run number. The data include performance parameters that were collected from the CACC application and data acquisition systems, including vehicle controller area network data, CARMA's MicroAutoBox, DSRC radios, and an independent measurement system. The tests were conducted at US Army’s Aberdeen Test Center located at Aberdeen Proving Grounds, MD. Further documentation can be found here: https://rosap.ntl.bts.gov/view/dot/1038.1last year
- This dataset details service and cost efficiency metrics for agencies reporting to the National Transit Database in the 2022 and 2023 report years. Only Full Reporters report data on Passenger Miles. The columns containing ratios have been calculated as the average across all reporting modes, not as the ratio of summed data. Thus, each transit agency received equal weight, regardless of that agency's total ridership. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Federal Funding Allocation, Operating Expenses, and Service database files. In years 2015-2021, you can find this data in the "Metrics" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the NTD data tables from before 2014, you can find data on metrics in the files called "Fare per Passenger and Recovery Ratio" and "Service Supplied and Consumed Ratios." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- Beginning in 2023, certain agencies are required to submit one week of service data on a monthly basis to comply with FTA’s Weekly Reference reporting requirement on form WE-20. This data release will therefore present the limited set of key indicators reported by transit agencies on this form and will be updated each month with the most current data. The resulting dataset provides data users with data shortly after the transit service was provided and consumed, over one month in advance of FTA’s routine update to the Monthly Ridership Time Series dataset. One use of this data is for reference in understanding ridership patterns (e.g., to develop to a full month estimate ahead of when the data reflecting the given month of service is released by FTA at the end of the following month). Generally, FTA has defined the reference week to be the second or third full week of the month. All sampled agencies will report data referencing the same reference week. The form collects the following service data points, as described in the metadata below: • Weekday 5-day UPT total for the reference week; • Weekday 5-day VRM total for the reference week; • Weekend 2-day UPT total for either the weekend preceding or following the reference week; and • Weekend 2-day VRM total for either the weekend preceding or following the reference week. • Vehicles Operated in Maximum Service (vanpool mode only) for the reference week. FTA has also derived the change from the prior month for the same agency/mode/type of service/data point. Users should take caution when aggregating this measure and are encouraged to use the dataset export to measure service trends at a higher level (i.e., by reporter or nationally). For any questions regarding this dataset, please contact the NTD helpdesk at ntdhelp@dot.gov .1last year
- Historic Highway Performance Monitoring System sample data for the year 20041last year
- Identifies the study field and study results that arise from ad hoc examination of items, usually inspected in support of a particular study or verification/refutation of a specific trend. This inspection type is a Level IV inspection.1last year
- *Dataset* Records for each BOC3 agent hired by a carrier/broker/freight forwarder. Each entity must hire a BOC3 agent to represent them in legal matters to obtain operating authority. In some cases, entities may act as their own BOC3 agent. The records in the dataset contain the BOC3 agent’s name and address. The dataset also contains the DOT number and docket number of the represented entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- The FMCSA Safety Measurement System (SMS) data, consists of summary results of all active Interstate and Intrastate Hazmat Motor Carriers of passengers only. File is comma delimited.1last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Signal Phasing and Timing Message (SPaT) Messages transmitted by road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, SPaT data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow SAE J2735 data frames (Section 6) and structure (Section 7). This dataset holds a flattened sample of the SPaT data from Tampa CV Pilot. A column of random numbers (randomNum) was added to allow for random sampling of data points within Socrata.1last year
- This dataset details vehicle types and ages for transit agencies reporting to the National Transit Database in the 2022 and 2023 report years. Vehicle types describe the vehicles employed in direct operation or support of transit service. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Vehicle Inventory and Service Vehicle Inventory database files. Rural reporters that operate in more than one state report their vehicles in only one of their states. In years 2015-2021, you can find this data in the "Vehicles" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.1last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data1last year
- last year
- *Dataset* Information on carrier/broker/freight forwarder authorities that have been revoked by FMCSA. The dataset includes the DOT number and docket number of the entity, the type of authority revoked, and the reason. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- Historic Highway Performance Monitoring System sample data for the year 20091last year
- Part of the Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data1last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Traveler Information Messages (TIMs) transmitted by road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, TIM data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow a SAE J2735 TIM message structure to convey important traffic information to onboard units (OBU) of equipped vehicles. Refer to SAE J2735 Section 5.16 Message: MSG_TravelerInformation Message (TIM). This dataset holds a flattened sample of the TIM data from Tampa CV Pilot. Three additional fields were added to this Socrata dataset during ETL: a geo column (travelerdataframe_msgId_position) to allow for mapping of the geocoded TIM data within Socrata, a random number column (randomNum) to allow for random sampling of data points within Socrata, and a time of day generated column (metadata_generatedAt_timeOfDay) to allow for filtering of data by generated time.1last year
- Licensed driver data from Highway Statistics table DL-22, broken down by state, sex, and age group.1last year
- This dataset shows Amtrak stations in opportunity zones1last year
- The FMCSA Safety Measurement System (SMS) data, consists of summary results of all active Interstate and Intrastate Hazmat Motor File Description: Carriers of property and/or passengers. File is comma delimited. One carrier per row.1last year
- This dataset details service schedules for Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Counties and Places: https://data.transportation.gov/Public-Transit/Demand-Response-Geographic-Area-Coverage-Counties-/3kum-6vpd/about_data Passenger Eligibility and Requirements: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Passenger/h9qc-expu/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Historic Highway Performance Monitoring System sample data for the year 20071last year
- This dataset contains a sample of the broadcast Traveler Information Messages (TIM) being generated by the Wyoming Connected Vehicle (CV) Pilot. This dataset only contains SchemaVersion 6 TIM sample data from December 18, 2018 to present. It is updated hourly and will hold up to 3 million of the most recent TIM records. The Schema Version 6 data is described further here. For sample TIM data prior to December 18, 2018, please refer to the Schema Version 5 dataset. The full set of TIMs can be found in the ITS Sandbox.1last year
- Identifies the type, make, company number, license plate, license plate state, VIN, CVSA Decal, and CVSA Number. There can be multiple Inspection Units per inspection.1last year
- Provides detailed fare information for highest and lowest fare markets under 750 miles. For a more complete explanation, please read the introductory information at the beginning of Table 5 itself in the report (https://www.transportation.gov/office-policy/aviation-policy/domestic-airline-consumer-airfare-report-pdf).1last year
- Historic Highway Performance Monitoring System sample data for the year 20081last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation.The Signal Plans for Roadside Equipment (RSE) data contains the basics of a Signal Phase and Timing (SPAT) message. This data includes SPAT message and the timestamp of the SPAT message. The data also provides the signal phase and timing information for one or more movements at an intersection.1last year
- The report includes inspections involving violations of the FMCSR or HRM.1last year
- The FMCSA New Entrant Safety Assurance Program out of service (OOS) data, consists of all entities that have received an OOS order from FMCSA. File is comma delimited.1last year
- This dataset details station/facility types and counts for each applicable agency reported to the National Transit Database for report years 2022 and 2023. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Facilities and Transit Stations database files. In years 2015-2021, you can find this data in the "Facilities and Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- last year
- The FMCSA Crash File contains data from state police crash reports involving drivers and vehicles of motor carriers operating in the U.S. Each report contains about 80 data elements pertaining to the motor carrier, driver, vehicles, and circumstances of a crash. Due to sensitive and/or privacy restrictions, driver, and hazardous materials data are not included in any crash files released to the public. The Crash File may contain multiple records for a crash. Separate reports are entered for each commercial motor vehicle involved in a crash. These multiple reports can be distinguished by the Crash Report Number field.1last year
- last year
- The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 3.0.1last year
- last year
- This data set shows Amtrak industrial, office, and commercial real estate in opportunity zones1last year
- The FMCSA Safety Measurement System (SMS) data, consists of active Intrastate Non-Hazmat Motor Carriers of passengers only. File is comma delimited. One carrier per row.1last year
- The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.1last year
- last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Highway-Rail-Grade-Crossing-Accident-Data/7wn6-i5b9.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form-55-Source-Table/unww-uhxd.1last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Basic Safety Messages (BSMs) generated by participant and public transportation vehicles onboard units (OBU) and transmitted to road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, BSM data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow SAE J2735 and J2945/1 standards and adopted units of measure. This dataset holds a flattened sample of the BSM data from Tampa CV Pilot. An extra geo column (coreData_position) was added to this dataset to allow for mapping of the geocoded BSM data within Socrata, and a column of random numbers (randomNum) was added to allow for random sampling of data points within Socrata.1last year
- The objective of this dataset is to create a location where there is a comprehensive list of all technologies, best practices and lessons learned from the Office of International Programs as a whole.1last year
- *Dataset* This dataset contains information on a carrier’s/broker’s/freight forwarder’s previous insurance policy(ies). This dataset contains the DOT number and docket number of the entity. Additionally, it contains the cancellation method (cancelled, replaced, name change, transferred), the type of policy, the policy number, and the effective and cancellation dates of the policy. All insurance information is related to the insurance policy either being cancelled, being replaced, or prior to a name change. It is not the subsequent (if applicable) policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- Massachusetts Department of Transportation (MassDOT) Work Zone Data Exchange (WZDx) v3.1 Feed SampleThis dataset provides information on work zones in the state of Massachusetts in a tabular format and is updated daily based on the live MassDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the MassDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. This live feed is currently compliant with the WZDx Specification v3.1.1last year
- The National Highway Construction Cost Index (NHCCI) is a price index that can be used both to track price changes associated with highway construction costs, and to convert current dollar expenditures on highway construction to real or constant dollar expenditures. This dataset contains the quarterly NHCCI estimates as well as the Seasonally Adjusted NHCCI and Component Contributions to Changes in NHCCI. Visit https://www.fhwa.dot.gov/policy/otps/nhcci/ for more information regarding the NHCCI.1last year
- Modal Service data and Safety & Security (S&S) public transit time series data delineated by transit/agency/mode/year/month. Includes all Full Reporters--transit agencies operating modes with more than 30 vehicles in maximum service--to the National Transit Database (NTD). This dataset will be updated monthly. The monthly ridership data is released one month after the month in which the service is provided. Records with null monthly service data reflect late reporting. The S&S statistics provided include both Major and Non-Major Events where applicable. Events occurring in the past three months are excluded from the corresponding monthly ridership rows in this dataset while they undergo validation. This dataset is the only NTD publication in which all Major and Non-Major S&S data are presented without any adjustment for historical continuity.1last year
- *Dataset* Records for all carriers/brokers/freight forwarders with active, inactive, or pending authorities (common or contract). It includes the DOT number and MC/FF/MX number for the carrier/broker/freight forwarder, along with company census data (e.g., types of authority, address, types of insurance on file, and amounts of insurance on file). See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- Federal and State field enforcement staff performs Inspections on Interstate and Intrastate Motor Carriers and Hazardous Materials carriers. Violations of the Federal Motor Carrier Safety Regulations (FMCSRs) severe enough may result in a vehicle and/or driver being placed "out-of-service." The data collected from inspection activity is collected and stored in the FMCSA Motor Carrier Management Information System (MCMIS) Inspection Data Files. Due to privacy restrictions, driver information is not included in any inspection files released to the public.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data is a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Injury-Illness-Summary-Operational-Data/m8i6-zdsy.1last year
- This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases. Artificial intelligence (AI) promises to drive the growth of the United States economy and improve the quality of life of all Americans. Pursuant to Section 5 of Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government," Federal agencies are required to inventory their AI use cases and share their inventories with other government agencies and the public. In accordance with the requirements of EO 13960, this spreadsheet provides the mechanism for federal agencies to create their inaugural AI use case inventories. https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government1last year
- The Highway Performance Monitoring System (HPMS) compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially‐enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.1last year
- The Company Census File contains records for active entities registered with FMCSA. Active entities include those entities subject to the FMCSR, HMR, or intrastate non-Hazardous Material (HM) carriers. To identify each entity, FMCSA assigns a unique number to each entity record. This number is referred to as the USDOT number. Each Census record contains entity identifying data, business operations data, equipment and driver data, and carrier review data.1last year
- Counts of Non-Major Safety and Security Events are reported to the National Transit Database on a monthly basis, by transit agency and transit mode. These include minor fires on transit property requiring suppression, transit worker assaults not involving transport for medical attention, and other safety events that are not reportable as Major Events because a Major Event reporting threshold is not met (see Safety and Security Events dataset for a list of Major Events). In this file you will find the number of occurrences or safety incidents per month and the number of injuries in Safety Events (Safety/Security = SAF) where an individual was immediately transported away from the scene for medical attention due to those occurrences. There will be one entry for any transit mode/location with at least one occurrence for the given month. The file also contains Transit Worker Assaults which did not immediately transport away from the scene for 2023-present, as well as other Security Events (Safety/Security = SEC) reported historically but no longer collected by FTA. Note that an assault involving transport away from the scene for medical attention meets the Injury threshold and is not counted in this dataset. Agencies are not required to provide details for these events, and any description provided is omitted. The description can be available upon request. Update 5/6/24: FTA has updated its validation procedure for Non-Major S&S events to allow for inclusion in the data publication sooner in certain cases. This month, users of this dataset may notice a larger increase in S&S events than normal for certain records in 2023-2024 (only years for which data collection and validation is presently ongoing) compared to prior releases. This was done to allow for a more timely release of validated data.1last year
- Dataset containing all of the Federal Funding Allocation inputs submitted by reporting transit agencies to the National Transit Database in the 2022 and 2023 report years. This reflects the most recently published data within the Federal Transit Administration's NTD Data website.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Injury-Illness-Summary-Casualty-Data/rash-pd2d.1last year
- All Railroads covered by Part 225 Accident/Injury reporting are required to provide monthly summary statistics via the form F6180.55.1last year
- This represents the Service data reported to the NTD by transit agencies to the NTD. In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj.1last year
- Contains all Basic Safety Messages (BSMs) collected during the Advanced Messaging Concept Development (AMCD) field testing program. For this project, all of the Part I BSM message fields were populated. Additional data fields were also added to the row to identify sender, time of communication, mode of communication, etc., allowing the consumer of this data set to accurately track messages through the system. All BSMs are generated by OBUs and ultimately received by the VCC Cloud server.1last year
- Provides fare premiums for airports in the top 1,000 city pairs, and demonstrates the impact of low-fare service and hub domination on fare levels. All records are aggregated as directionless city pair markets. Air traffic in each direction is combined. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- Data summarized by city, includes the number of city-pair markets in the top 1,000 in either comparison period that involve each city, the number of passengers traveling to and from each city, the average fare, average fare per mile (yield), and average distance traveled. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- This dataset is the source dataset and contains raw data values. It will replace the current data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/DownloadCrossingInventoryData.aspx) when the safetydata.fra.dot.gov site is decommissioned in 2024. To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Current/m2f8-22s6.1last year
- This is a list of all Major Safety and Security Events from January of 2014 to the most recently published data within the Federal Transit Administration's major event time series.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form57-Source-Table/icqf-xf4w.1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form55a-Source-Table/kuvg-3uwp.1last year
- Tens of millions of vehicles with Takata air bags are under recall. Long-term exposure to high heat and humidity can cause these air bags to explode when deployed. Such explosions have caused injuries and deaths. NHTSA urges vehicle owners to take a few simple steps to protect themselves and others from this very serious threat to safety. This dataset tracks various progress indicators for the recall.1last year
- This dataset provides information on work zones in the state of North Carolina in a tabular format and is updated daily based on the live NCDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the NCDOT WZDx feed data can be found at the ITS WorkZone Raw Data Sandbox and the ITS Work Zone Semi-Processed Data Sandbox. The live feed is currently compliant with the WZDx Specification v3.1.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5).1last year
- Annual motor vehicle registrations by vehicle type and state, from Highway Statistics table MV-1.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).1last year
- This dataset is in a user-friendly human-readable format. It contains the historical crossing inventory. To download the current inventory data, go here: https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Form-71-Current/m2f8-22s6.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).1last year
- This dataset provides lane closure occurrences within the Texas Department of Transportation (TxDOT) highway system in a tabular format. A continuously updating archive of the TxDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. The live feed is currently compliant with the Work Zone Data Exchange (WZDx) Specification version 2.0.1last year
- This dataset is in a user-friendly human-readable format. It contains the current crossing inventory - one record for each crossing. To download historical data, go here: https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Historical/vhwz-raag. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Crossing-Inventory-Source-Data-Form-71-Current/xp92-5xme.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr).1last year
- This is list of data elements and their attributes that are used by data assets at the Federal Highway Administration.1last year
- This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form-54-Source-Table/aqxq-n5hy.1last year
- Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- This dataset contains the estimates of the vehicle miles traveled (VMT) for interstate highways and how the total travel measured by VMT compares with travel that occurred in the same week of the previous year.1last year
- Available on the internet only, this table is an expanded version of Table 1 that lists all city-pair markets in the contiguous United States that average at least 10 passengers each day. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- About the Data: The dataset includes recall information related to specific NHTSA campaigns. Users can filter based on characteristics like manufacturer and component. The dataset can also be filtered by recall type: tires, vehicles, car seats, and equipment. The earliest campaign data is from 1966. The dataset displays the completion rate from the latest Recall Quarterly Report or Annual Report data from Year 2015 Quarter 1 (2015-1) onward. Data Reporting Requirement: Manufacturers who determine that a product or piece of original equipment either contains a safety defect or is not in compliance with Federal safety standards are required to notify NHTSA within 5 business days. NHTSA requires that manufacturers file a Defect and Noncompliance Report in compliance with Federal Regulation 49 (the National Traffic and Motor Safety Act) Part 573, which identifies the requirements for safety recalls. This information is stored in the NHTSA database referenced above. Notes: The default visualization depicted here represents only the top 12 manufacturers for the current calendar year. Please use the Filters for specific data requests. For a complete historical perspective, please visit: https://www.nhtsa.gov/sites/nhtsa.gov/files/2023-03/2022-Recalls-Annual-Report_030223-tag.pdf.1last year
- Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf1last year
- Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form-54-Source-Table/aqxq-n5hy.1last year
- This dataset contains the estimates of the vehicle miles traveled (VMT) for interstate highways and how the total travel measured by VMT compares with travel that occurred in the same week of the previous year.1last year
- This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.1last year
- The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).1last year
- Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- Available on the internet only, this table is an expanded version of Table 1 that lists all city-pair markets in the contiguous United States that average at least 10 passengers each day. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports1last year
- Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf1last year
- About the Data: The dataset includes recall information related to specific NHTSA campaigns. Users can filter based on characteristics like manufacturer and component. The dataset can also be filtered by recall type: tires, vehicles, car seats, and equipment. The earliest campaign data is from 1966. The dataset displays the completion rate from the latest Recall Quarterly Report or Annual Report data from Year 2015 Quarter 1 (2015-1) onward. Data Reporting Requirement: Manufacturers who determine that a product or piece of original equipment either contains a safety defect or is not in compliance with Federal safety standards are required to notify NHTSA within 5 business days. NHTSA requires that manufacturers file a Defect and Noncompliance Report in compliance with Federal Regulation 49 (the National Traffic and Motor Safety Act) Part 573, which identifies the requirements for safety recalls. This information is stored in the NHTSA database referenced above. Notes: The default visualization depicted here represents only the top 12 manufacturers for the current calendar year. Please use the Filters for specific data requests. For a complete historical perspective, please visit: https://www.nhtsa.gov/sites/nhtsa.gov/files/2023-03/2022-Recalls-Annual-Report_030223-tag.pdf.1last year
- Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.1last year
- last year
- Annual number of licensed drivers by sex and age groups from FHWA Highway Statistics table DL-220.1last year
- Annual number of licensed drivers for the 50 states and DC from FHWA Highway Statistics table DF-201.1last year
- Annual vehicle miles of travel by functional system for each of the 50 states, DC, and Puerto Rico from the Highway Statistics table VM-2. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)1last year
- Miles of public roads by the roadway's functional system and whether rural or urban for the 50 states, DC, and Puerto Rico (from 1996) from FHWA Highway Statistics table HM-60. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)1last year
- The attached file has trip level information for Health Connector rides taken from October 1, 2024 to January 31, 2025. Any information that could identify the rider has been stripped. Each row corresponds to one ride request.1last year
- Annual length of U.S. public roads in miles by functional system for each of the 50 states, DC, and Puerto Rico from the Highway Statistics table HM-20. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland 2019 Average Annual Daily Traffic data1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data without rows where road_tmc and road are inconsistent.1last year
- Annual state reported motor vehicle registration data published in Highway Statistics table MV-1.1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data1last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is the processed integrated traffic data with work zone and incident information. Attached below are the number of lanes data and impacted work zone .pkl file.1last year
- Historic Highway Performance Monitoring System sample data for the year 19991last year
- Historic Highway Performance Monitoring System sample data for the year 19901last year
- Historic Highway Performance Monitoring System sample data for the year 19941last year
- Historic Highway Performance Monitoring System universe data for the year 19851last year
- Historic Highway Performance Monitoring System universe data for the year 19961last year
- Historic Highway Performance Monitoring System universe data for the year 20041last year
- Historic Highway Performance Monitoring System sample data for the year 19961last year
- Historic Highway Performance Monitoring System sample data for the year 19931last year
- Historic Highway Performance Monitoring System sample data for the year 19911last year
- Historic Highway Performance Monitoring System universe data for the year 19921last year
- Historic Highway Performance Monitoring System universe data for the year 20021last year
- Historic Highway Performance Monitoring System sample data for the year 19921last year
- Historic Highway Performance Monitoring System universe data for the year 19871last year
- Historic Highway Performance Monitoring System universe data for the year 19911last year
- Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is a raw sample of Maryland roadway speed data1last year
- Historic Highway Performance Monitoring System universe data for the year 20061last year
- Historic Highway Performance Monitoring System universe data for the year 20051last year
- Historic Highway Performance Monitoring System universe data for the year 19981last year
- Historic Highway Performance Monitoring System sample data for the year 19891last year
- Historic Highway Performance Monitoring System sample data for the year 19881last year
- Historic Highway Performance Monitoring System universe data for the year 19951last year
- Historic Highway Performance Monitoring System universe data for the year 19971last year
- Historic Highway Performance Monitoring System universe data for the year 19901last year
- Historic Highway Performance Monitoring System universe data for the year 20001last year
- Historic Highway Performance Monitoring System universe data for the year 19931last year
- Historic Highway Performance Monitoring System sample data for the year 19801last year
- Historic Highway Performance Monitoring System universe data for the year 19861last year
- last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 20031last year
- Historic Highway Performance Monitoring System sample data for the year 19951last year
- last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 20071last year
- Historic Highway Performance Monitoring System sample data for the year 20001last year
- last year
- last year
- 1last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 20011last year
- Historic Highway Performance Monitoring System universe data for the year 19941last year
- last year
- last year
- State DOT will provide VMT. This data is summarized by Paved and Unpaved and by Vehicle Type.1last year
- 1last year
- Historic Highway Performance Monitoring System universe data for the year 20011last year
- 1last year
- last year
- last year
- last year
- last year
- 1last year
- Historic Highway Performance Monitoring System universe data for the year 19891last year
- last year
- Annual gallons of taxed motor fuel for the 50 states and DC from FHWA Highway Statistics table MF-202.1last year
- last year
- last year
- State DOT will provide Local Vehicle-Miles-Traveled (VMT) summarized by FHWA Adjusted Urban Area.1last year
- last year
- last year
- Historic Highway Performance Monitoring System universe data for the year 19881last year
- last year
- State DOT will provide Local Vehicle-Miles-Traveled (VMT) summarized by FHWA Adjusted Urban Area.1last year
- Historic Highway Performance Monitoring System universe data for the year 20081last year
- 1last year
- Test case WFCW-1 Results - FCW Stopped Vehicle Rep 21last year
- The table displays the total number of licensed drivers in each State. The table shows the number of male and female licensed drivers by sex and age group.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel.1last year
- State DOT will provide VMT. This data is summarized by Paved and Unpaved and by Vehicle Type.1last year
- This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.1last year
- Historic Highway Performance Monitoring System universe data for the year 19991last year
- WFCW-2 Stopped Vehicle Message Prioritization Rep 21last year
- Historic Highway Performance Monitoring System sample data for the year 19861last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The North contains data for the following States: Maine, New Hampshire, Vermont, New York, Massachusetts, Rhode Island, Connecticut, New Jersey, Pennsylvania, Ohio, Maryland, District of Columbia, and Delaware.1last year
- Historic Highway Performance Monitoring System universe data for the year 19831last year
- Historic Highway Performance Monitoring System data sample for the year 19981last year
- The main dataset is a 70 MB file of trajectory data (I294_L1_final.csv) that contains position, speed, and acceleration data for small and large automated (L1) vehicles and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for ten distinct data collection “Runs” (I294_L1_RunX_with_lanes.png, where X equals 8, 18, and 20 for southbound runs and 1, 3, 7, 9, 11, 19, and 21 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L1-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L1.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L1_Lane-2.png through I294_L1_Lane-5.png and the northbound lanes are shown visually in I294_L1_Lane2.png through I294_L1_Lane5.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day. As part of this dataset, the following files were provided: I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X. I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane cent1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin.1last year
- State DOTs will provide Local and Rural Minor Collector Mileage summarized by county, ownership, and Paved and Unpaved. This data is provided in a direct input by the State DOTs.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The South contains data for the following States: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia, and Puerto Rico.1last year
- The Facility Inventory dataset details all facilities supporting public transit service as reported to the National Transit Database (NTD) by each public transit agency in the 2023 report year. This file is also published at https://www.transit.dot.gov/ntd/ntd-data, under the Product Type of "Annual Database (Excel)." Equivalent datasets from 2018 through 2022 can also be found using that link. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- last year
- last year
- 1last year
- The Summary Statistics dashboard includes rural and urban measures for roadway mileage, lane miles, vehicle miles traveled, fatalities, and fatality rate.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The South contains data for the following States: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia, and Puerto Rico.1last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The North contains data for the following States: Maine, New Hampshire, Vermont, New York, Massachusetts, Rhode Island, Connecticut, New Jersey, Pennsylvania, Ohio, Maryland, District of Columbia, and Delaware.1last year
- last year
- This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.1last year
- The main dataset is a 9 MB file of trajectory data (I294_L2_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for twelve distinct data collection “Runs” (I294_L2_Run_X_ref_image_with_lanes.png, where X equals 5, 28, 30, 36, 38, and 42 for southbound runs and 23, 29, 31, 33, 35, and 41 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L2-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L2.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L2_lane-2.png through I294_L2_lane-5.png and the northbound lanes are shown visually in I294_L2_lane2.png through I294_L2_lane5.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided: I294_L2_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the L2 test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I294_L2_Run_X_ref_image_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X. I294_L2_Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. T1last year
- Summary monthly motor fuel data on the amount of on-highway fuel used at the national level. Includes the amount of gallons of gasoline/gasohol and special fuel (primarily diesel) taxed each month.1last year
- Summary monthly motor fuel data on the amount of on-highway fuel used at the state level. Includes the amount of gallons of gasoline/gasohol and special fuel (primarily diesel) taxed each month.1last year
- Historic Highway Performance Monitoring System universe data for the year 19801last year
- The main dataset is a 350 MB file of trajectory data (TGSIM-Foggy Bottom-Data.csv) that contains position, speed, and acceleration data for pedestrians, bicycles, scooters, non-automated passenger cars, automated vehicles, motorcycles, buses, and trucks in an urban environment. Supporting files include an aerial reference image (Reference_Image_Foggy Bottom.png) and a list of polygon boundaries (Foggy_Bottom_boundaries.txt) and associated images (i1.png, i2.png, …, i49.png stored in the folder titled “Annotation on Regions.zip”) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset. As part of this dataset, the following files were provided: TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.0186613838586-meter conversion. Reference_Image_Foggy Bottom.png is the aerial reference image that defines the geographic region and the associated roadway segments. Foggy_Bottom_boundaries.txt contains the coordinates that define the roadway segments (n = 49). Each polygon is a list of four to six coordinate pairs measured in pixels (which can be converted to meters using the provided 1 pixel = 0.0186613838586-meter conversion), with (0,0) global reference coordinates at the top-left of the reference image. Annotation on Regions.zip, which includes i1.png, i2.png,..., i49.png, are images that visually map the road segment IDs (indicated by the number following the i in the file name) to the reference image.1last year
- The main dataset is a 232 MB file of trajectory data (I395-final.csv) that contains position, speed, and acceleration data for non-automated passenger cars, trucks, buses, and automated vehicles on an expressway within an urban environment. Supporting files include an aerial reference image (I395_ref_image.png) and a list of polygon boundaries (I395_boundaries.csv) and associated images (I395_lane-1, I395_lane-2, …, I395_lane-6) stored in a folder titled “Annotation on Regions.zip” to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns “x1” to “x5” represent the horizontal pixel values in the reference image, with “x1” being the leftmost boundary line and “x5” being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. The origin point of the reference image is located at the top left corner. The dataset defines five lanes with five boundaries. Lane -6 corresponds to the area to the left of “x1”. Lane -5 corresponds to the area between “x1” and “x2”, and so forth to the rightmost lane, which is defined by the area to the right of “x5” (Lane -2). Lane -1 refers to vehicles that go onto the shoulder of the merging lane (Lane -2), which are manually separated by watching the videos. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments. I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns "x1" to "x5" represent the horizontal pi1last year
- Historic Highway Performance Monitoring System sample data for the year 19831last year
- Counts of Non-Major Safety and Security Events are reported to the National Transit Database on a monthly basis, by transit agency and transit mode. These include minor fires on transit property requiring suppression, transit worker assaults not involving transport for medical attention, and other safety events that are not reportable as Major Events because a Major Event reporting threshold is not met (see Safety and Security Events dataset for a list of Major Events). This file includes event data reported to the National Transit Database (NTD) for Commuter Rail (CR) and Alaska Railroad (AR) modes, as well as Heavy Rail (HR) service reported for Port Authority Trans Hudson (NTD ID: 20098), Hybrid Rail (YR) service for the Tri-County Metropolitan Transportation District of Oregon (NTD ID: 00008), Hybrid Rail (YR) service for Denton County Transportation Authority (NTD ID: 60101), and Hybrid Rail (YR) service for Capital Metropolitan Transportation Authority (NTD ID: 60048). Because these services fall under the safety oversight of the Federal Railroad Administration, the agencies are not required to report Safety Events (e.g., collisions, derailments, etc.) to the Federal Transit Administration through the NTD. Security events occurring on transit-owned property for these entities are reported to NTD, but excluded from other files to preserve the integrity of those datasets. They are presented in this file for completeness and should be considered by any user attempting to understand the scope and scale of reportable Security Events reported by public transit operators.1last year
- The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for six distinct data collection “Runs” (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each “Run” (I-90-stationary-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94Stationary.csv” for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X. I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and ve1last year
- Historic Highway Performance Monitoring System sample data for the year 1980.1last year
- last year
- last year
- This dataset offers insight on weekly fluctuation of the gasoline product supply, which is an important part of any analysis of construction trends, materials and operating costs associated with highway repair and construction, and changes in traffic volume. These data come directly from the Energy Information Administration (EIA) website. The EIA publishes the average daily amount of gasoline supplied in barrels, which HPPI converts to an average number of gallons of gasoline per week.1last year
- This report contains tables and charts on the financial condition of the U.S. major airlines. All data presented in this financial and traffic review are derived from data reported to the U.S. Department of Transportation on Form 41 Schedules by Large Certificated Air Carriers. The data are presented on both a carrier group and an individual carrier basis, but the primary focus is on the individual major carrier and its performance. Data are presented for the most recent quarterly period and the comparable quarter a year earlier and also on a 12-month ended basis as at the end of the five most recent quarters. In addition, data on charges over comparable periods 12-months earlier are presented. A graphic presentation of comparative trends, on a carrier group basis, is made for several unit and overall financial indicators. In the case of merged carriers, data for the carriers involved have been combined and presented under the name of the surviving carrier so that meaningful comparisons could be made for the most recent 18 quarters. Also, carriers can move between groupings (Majors and Nationals) based on the criteria listed below over time. Each report includes 18 quarters of data. In the instance where a carrier falls into both groupings during the 18 quarters, a carrier will appear in both reports. The data from the Majors report and the data from the Nationals report should not be combined without ensuring any duplications are removed. Carrier Group Definitions Majors: Air carriers with annual operating revenues exceeding $1,000,000,000 Nationals: Air carriers with annual operating revenues between $100,000,000 and $1,000,000,000 Large Regionals: Air carriers with operating revenues between $20,000,000 and $99,000,000 Medium Regionals: Carriers with annual operating revenues less than $19,999,999 or that operate only aircraft with 60 seats or less (or 18,000 lbs maximum payload) https://www.transportation.gov/policy/aviation-policy/airline-quarterly-financial-review1last year
- Historic Highway Performance Monitoring System sample data for the year 19821last year
- Historic Highway Performance Monitoring System sample data for the year 19851last year
- last year
- This a reference table for the Grade Crossing Inventory System, which is the application used to submit data for the Highway-Rail Grade Crossing Inventory (Form 71). The data dictionary for GCIS is attached as well. The LookupType column contains the name of the field/column in the source GCIS/Form 71 dataset. The LookupValue column contains the submitted value and the LookupText field is the human-readable text description of that value (e.g. for LookupType=TypeXing; LookupValue=3 and LookupText=Public, which designates that a crossing is public). This reference table can be used for the Crossing Inventory Source Data Form 71 – Current: https://datahub.transportation.gov/dataset/Crossing-Inventory-Source-Data-Form-71-Current/xp92-5xme.1last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 19841last year
- 2019 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- last year
- This dataset details the ages of guideway elements for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Guideway elements include elements, structures, or facilities dedicated specifically to transit use, such as track, subway structures, tunnels, bridges, and propulsion power systems. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Way Mileage database files. In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the data tables from before 2015, you can find data on vehicles in the file called "Transit Way Mileage - Rail Modes." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details funding from taxes levied by each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- 2018 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- Historic Highway Performance Monitoring System sample data for the year 19801last year
- As of Report Year (RY) 2023, FTA requires that reporters with fixed route modes create and maintain a public domain general transit feed specification (GTFS) dataset that reflects their fixed route service. This specification allows for the mapping and other geospatial data visualization and analyses of key transit elements such as stops, routes, and trips. At least one GTFS weblink is provided by the transit agency for each fixed route bus mode and type of service. These include all Rail modes as well as Bus, Bus Rapid Transit, Commuter Bus, Ferryboat and Trolleybus. GTFS requires that an overarching compressed file contain, at a minimum, seven underlying text files: (a) Agency; (b) Stops; (c) Routes; (d) Trips; (e) Stop Times; (f) Calendar or Calendar Dates.txt; and (g) Feed Info.txt. An eighth file, Shapes.txt, is an optional file. FTA collects and publishes these links for further analysis using related GTFS files. FTA is not responsible for managing the websites that host these files, and users with questions regarding the GTFS data are encouraged to contact the transit agency. In many cases, publicly hosted weblinks could not be provided (i.e., due to constraints within the transit agency), but the agency was able to produce a zip file of the required GTFS data. Demand Response, Vanpool, and other non-fixed route modes are excluded. The column "Alternate Format" indicates that the agency provided FTA a weblink in an alternate format with some justification for doing so. The file "Waived" indicates that no GTFS files were produced and FTA granted the agency a waiver from the requirement in Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2023 General Transit Feed Specification database file. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details directly generated funding for each agency. Examples include Fares, Concessions and Advertising. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details state funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include General and Transportation funds. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In report year 2022, Extraordinary and Special Item Funds were reported under General Funds. In report year 2023, this was separated into its own category. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Volume of taxed special fuel, primarily diesel, but including alternative fuels, reported by the States each month, based on reports from suppliers and distributors. These amounts are reported in various Office of Highway Policy Information (OHPI) products including the longstanding Monthly Motor Fuel Report, and the annual Highway Statistics publications.1last year
- last year
- 2020 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- This dataset details local funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Examples include Income, Sales, Property and Fuel taxes and Tolls. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The motor vehicle registration dashboard shows the number and type of vehicle (automobile, truck, motorcycle, and bus) registered over time in each state.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- Volume of gasoline reported by the States each month, based on reports from suppliers and distributors. These amounts are reported in various Office of Highway Policy Information (OHPI) products including the longstanding Monthly Motor Fuel Report, and the annual Highway Statistics publications.1last year
- Historic Highway Performance Monitoring System sample data for the year 20051last year
- This dataset details track and roadway mileage/characteristics for each agency, mode, and type of service, as reported to the National Transit Database in Report Years 2022 and 2023. These data include the types of track/roadway elements employed in transit operation, as well as the length and/or count of certain elements. NTD Data Tables organize and summarize data from the 2022 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Way Mileage database files. In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the data tables from before 2015, you can find corresponding data in the file called "Transit Way Mileage - Rail Modes" and "Transit Way Mileage - Non-Rail Modes." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Select summary highway statistics, 1980 - 2017, mileage, lane-miles, vehicle miles traveled, and fatalities by state and functional system.1last year
- 2017 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. Basic Safety Messages (BSM) sent by connected vehicles (CVs) through either the cellular network or Dedicated Short Range Communication (DSRC) when the vehicle is in the range of Roadside Units (RSU). These messages were received by the traffic management center (TMC).1last year
- The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the National Center for Environmental Information – National Oceanic and Atmospheric Administration. Precipitation information from this data source is used in the cluster analysis.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- Contains all PDCMs generated during the AMCD field testing program. The PDCM is a control message sent from the server to OBUs to customize a request for Probe Vehicle Data (PVD) from the receiving OBU. All PDCMs are generated by the VCC Cloud server and transmitted to OBU clients through either a DSRC or cellular communications channel.1last year
- Historic Highway Performance Monitoring System data sample for the year 19971last year
- This dataset reports the historical National Highway System 50th percentile median speeds for various roadway types, months, and vehicles on US roads.1last year
- This dataset details federal funding sources for each applicable agency reporting to the NTD in the 2022 and 2023 report years. Federal funding sources are financial assistance obtained from the Federal Government to assist with the costs of providing transit services. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains queue warning messages that were recommended by the INFLO Q-WARN algorithm and sent by the traffic management center to vehicles to warn drivers upstream of the queue. The objective of queue warning is to provide a vehicle operator sufficient warning of impending queue backup in order to brake safely, change lanes, or modify route such that secondary collisions can be minimized or even eliminated.1last year
- About the Data The dataset includes publicly available NHTSA investigation information related to the identification and correction of safety-related defects in motor vehicles and vehicle equipment. For more information on NHTSA investigations, including safety defect investigations, please visit https://www.nhtsa.gov/resources-investigations-recalls.1last year
- Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.1last year
- 2016 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.1last year
- The data attached and/or displayed were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. A BSM is one of the messages belonging to the Society of Automotive Engineers (SAE) J2735 Standard. This standard is geared toward supporting the interoperability of DSRC applications through the use of a standardized message set and its data frames and data elements. A BSM, which is at times referred to as a “heartbeat” message, is a frequently transmitted message (usually at approximately 10Hz) that is meant to increase a vehicle’s situational awareness. These messages are intended to be used for a variety of applications to exchange safety data regarding a vehicle’s state. A typical BSM contains up to two parts. Part I, the binary large object (blob), is included in every BSM. It contains the fundamental data elements that describe a vehicle’s position (latitude, longitude, elevation) and motion (heading, speed, acceleration). Part II of a BSM contains optional data that is transmitted when required or in response to an event. Typically Part II contains data that serves as an extension of vehicle safety information (path history, path prediction, event flags) and data pertaining to the status of a vehicle’s components, such as lights, wipers, and brakes. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Messages such as MAP, Detectors, and Simulation results1last year
- This dataset details places and counties served by Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Passenger Eligibility and Requirements: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Passenger/h9qc-expu/about_data Service Schedules: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Service-S/4p55-emkp/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the Caltrans – Performance Measurement System (PeMS). Speed data from this dataset were used to derive the freeway travel time. There are three separate text files with one for each operational condition.1last year
- This dataset details operating expenses for each applicable agency, mode, and type of service (TOS), split by expense type reporting to the National Transit Database in the 2022 and 2023 report years. Expense types include Vehicle Operations, General Administration, and more. Only Full Reporters report expenses by function and type. Expenses from other reporter types are included under Reduced Reporter Expenses. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Operating Expenses database files. In years 2015-2021, you can find this data in the "Operating Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Massachusetts Department of Transportation (MassDOT) Work Zone Data Exchange (WZDx) v2.0 Feed SampleThis dataset provides information on work zones in the state of Massachusetts in a tabular format and is updated daily based on the live MassDOT Work Zone Data Exchange (WZDx) Feed. A continuously updating archive of the MassDOT WZDx feed data can be found at ITS WorkZone Raw Data Sandbox and the ITS WorkZone Semi-Processed Data Sandbox. The live feed is currently compliant with the WZDx Specification v2.0.1last year
- This dataset details capital expenses by capital use type (existing or expansion) for each applicable agency, mode, and type of service (TOS) reporting to the National Transit Database in the 2022 and 2023 report years. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Capital Use database files. In years 2015-2021, you can find this data in the "Capital Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- This dataset details operating expenses for each applicable agency, mode, and type of service (TOS), split by expense type or "Object Class" reporting to the National Transit Database in the 2022 and 2023 report years.. Object classes include salaries and wages, fuel, and others. Only Full Reporters report expenses by function and type. Expenses from other reporter types are included under Reduced Reporter Expenses. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Operating Expenses database files. In years 2015-2021, you can find this data in the "Operating Expenses" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- This dataset details funding sources for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years, split by fund expenditure type: capital and operating. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Sources database files. In years 2015-2021, you can find this data in the "Funding Sources" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The Federal Highway Administration (FHWA) has been receiving Highway inventory, usage, condition and performance data from State Departments of Transportation (DOT) since 1978 to support the program mission of the FHWA. Specifically, HPMS consists of detailed road segment data (63 Attributes) for higher order systems. Sample attributes for collector systems and summary data for the local roads. New requirements for HPMS took effect in 2014 that required each State DOTs to expand their Linear Referencing Systems (LRS), a statewide geospatial representation of their road system that includes all public roads. This requirement was put in place to support highway safety. States DOTs submit HPMS data annually to the FHWA following a prescribed format outlined in the Highway Performance Monitoring System Field Manual.1last year
- Intelligent Network Flow Optimization Prototype Infrastructure Traffic Sensor System Data AggregatorData is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains real-time volume, speed and loop occupancy data that were collected from WSDOT’s simulated roadway sensors every 20 seconds and aggregated according to user defined procedures and threshold by the Infrastructure Traffic Sensor System (TSS) Data Aggregator software.1last year
- Historic Highway Performance Monitoring System sample data for the year 20021last year
- This a list of active and inactive railroads, companies, and other organizations related to railroad operations. Organization Type ID = 1 designates a railroad; 4 designates a non-railroad organization (e.g. company, shipper, public entity, etc.). If a code has a blank EndDate, this means the organization is active; a populated EndDate field means the organization is no longer active.1last year
- State DOTs will provide Local and Rural Minor Collector Mileage summarized by county, ownership, and Paved and Unpaved. This data is provided in a direct input by the State DOTs.1last year
- This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program.The freeway data consists of two months of data (Sept 15 2011 through Nov 15 2011) from dual-loop detectors deployed in the main line and on-ramps of a Portland-area freeway. The section of I-205 NB covered by this test data set is 10.09 miles long and the section of I-205 SB covered by this test data set is 12.01 miles long The data includes: flow, occupancy, and speed.1last year
- Motor Vehicle Registration Data by Energy Source :2016 -Present Vehicle types are compatible with FHWA Highway Statistics VM-1 "Total" counts of vehicles for a year are compatible with FHWA Highway Statistics MV-1 minus "Motorcycle." Motorcycle data are not included.1last year
- last year
- last year
- This dataset details stations for each agency and mode for stations reported to the National Transit Database in report years 2022 and 2023. These data include the type of facility and the decade in which it was built. In many cases, stations are reported by each mode and type of service that uses them. For example, a single station used by bus - directly operated, bus - purchased transportation, and commuter bus - directly operated would be reported three times. For more detail, please see the NTD Policy Manual. Rural reporters do not report passenger stations and are not included in this file. Modes Demand Response, Demand Response - Taxi, Vanpool, and Publico also do not report stations and are also excluded. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Facility Inventory database files. In years 2015-2021, you can find this data in the "Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- This file includes event data reported to the National Transit Database (NTD) for Commuter Rail (CR) and Alaska Railroad (AR) modes, as well as Heavy Rail (HR) service reported for Port Authority Trans Hudson (NTD ID: 20098), Hybrid Rail (YR) service for the Tri-County Metropolitan Transportation District of Oregon (NTD ID: 00008), Hybrid Rail (YR) service for Denton County Transportation Authority (NTD ID: 60101), and Hybrid Rail (YR) service for Capital Metropolitan Transportation Authority (NTD ID: 60048). Because these services fall under the safety oversight of the Federal Railroad Administration, the agencies are not required to report Safety Events (e.g., collisions, derailments, etc.) to the Federal Transit Administration through the NTD. Security events occurring on transit-owned property for these entities are reported to NTD, but excluded from other files to preserve the integrity of those datasets. They are presented in this file for completeness and should be considered by any user attempting to understand the scope and scale of reportable Security Events reported by public transit operators.1last year
- last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- last year
- Contains metrics describing service consumption and service cost for each public transportation agency, by mode and type of service.1last year
- This dataset details mechanical failures for each applicable agency, mode, and type of service (TOS) reporting to the National Transit Database in the 2022 and 2023 report years. Only Full Reporters report breakdowns. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Vehicle Maintenance database files. In years 2015-2021, you can find this data in the "Breakdowns" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- GPS pings collected by study participants who rode conventional and e-bikes at Minute Man National Historic Park between April and September 2022.1last year
- last year
- This dataset details maintenance facility capacities and counts for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years. Please note that because Rural Reporters are not required to report facility size counts, for these reporters null values appear under facility size columns, yet non-zero values may appear under Total Facilities. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Facilities database files. In years 2015-2021, you can find this data in the "Maintenance Facilities" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.1last year
- FRA develop a spatial point layer of the rail bridges over road and water. The bridges are a snapshot and is not an offical or complete inventory of all bridges. Railroads change ownership, railroads are abandoned, bridges are replaced, etc. therefore it cannot be relied upon as being accurate.1last year
- FRA develop a spatial point layer of the rail bridges over road and water. The bridges are a snapshot and is not an offical or complete inventory of all bridges. Railroads change ownership, railroads are abandoned, bridges are replaced, etc. therefore it cannot be relied upon as being accurate.1last year
- Contains all Basic Mobility Messages (BMMs) collected during the Advanced Messaging Concept Development (AMCD) field testing program. While there is no specific standard in existence that addresses the content of a BMM, the descriptive definitions of the variables were derived from the J2735 standard where applicable. All BMMs are generated by OBUs and ultimately received by the VCC Cloud server.1last year
- Data represent the performance of prototype cooperative automated driving system applications for improving traffic mobility. The applications include the integrated highway prototype that consists of vehicle platooning, speed harmonization, and automated lane change and merge.1last year
- This dataset details passenger eligibility and requirements for Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Counties and Places: https://data.transportation.gov/Public-Transit/Demand-Response-Geographic-Area-Coverage-Counties-/3kum-6vpd/about_data Service Schedules: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Service-S/4p55-emkp/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The main dataset is a 130 MB file of trajectory data (I90_94_moving_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for four distinct data collection “Runs” (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each “Run” (I-90-moving-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94moving.csv” for more details). The dataset defines six northbound lanes using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided: I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X. I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes. Annotation on Regions.zip, which includes images that visually map lanes (I90_91last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The GPS data set catalogs the vehicle operation data of the test vehicles that used for the MMITSS field testing. The data contains the performance and operation details of vehicles. This file contains a number of fields detailing elements such as vehicle position and speed, fidelity measures of GPS-based data elements, and vehicle operation data. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message1last year
- This dataset consists of truck size and weight enforcement data including number of trucks weighed, number of violations, and number of oversize/overweight permits, as reported by the States in their annual certification to FHWA.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2VMCT-1 Verify V2V communication of BSMs vehicle 1 data1last year
- last year
- The data in this repository were collected from the San Diego, California testbed, namely, I-15 from the interchange with SR-78 in the north to the interchange with SR-163 in the south, along the mainline and at the entrance ramps. This file contains information on the field observation and simulation results for speed profile from the Dallas, Texas testbed. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.1last year
- This dataset contains a one-month sample of flattened EVENT data records from the New York City (NYC) Connected Vehicle (CV) Pilot that have undergone obfuscation of precise time and location details as well as other vehicle identifiers. The full unflattened event data from NYC CV pilot can be found in the ITS Sandbox. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.1last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2VMCT-1 Verify V2V communication of BSMs vehicle 2 data1last year
- This data represents HPMS Sample limits that correspond to the HPMS Section Data. This dataset contains expansion factors that are used to expand the attributes to State wide aggregation. More information regarding the Sample dataset is contained in the HPMS Field Manual. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin1last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The Vehicle Trajectories file is populated with basic safety messages received from equipped vehicle within the communication range of an Roadside Equipment (RSEs). The data also contains elements that communicate additional details about the vehicle that is used for vehicle safety applications, and elements that communicate specific items of a vehicle‘s status that are used in data event snapshots which are gathered and periodically reported to an RSEs. These data are transmitted at a rate of 10 Hz. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message1last year
- last year
- last year
- State DOTs provide the location limits of highway sections to be used to represent statewide aggregations based on a statistically valid Sample Panel. The Mid-America contains data for the following States: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wisconsin.1last year
- last year
- The main, combined file that is used for the 4 Views for each type: Departures, Freight, Seats, and Passengers. This combined dataset will not be published, but the 4 views will be published separately.1last year
- This is the EVENT data captured from the New York City CV Pilot project that was processed by the independent evaluators at Volpe. Additional data collected and data dictionary are in the attachments. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.1last year
- Contains ratios describing service and cost for each agency, mode, and type of service.1last year
- This dataset contains data on transit agency employees as reported to the National Transit Database in the 2022 and 2023 report years. It is organized by agency, mode, type of service, and Employee Type (Full Time or Part Time Employee). The NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis This dataset is based on the 2022 and 2023 Employees database files, which are published to the NTD at https://transit.dot.gov/ntd/ntd-data. Only Full Reporters report data on employees, and only for Directly Operated modes. Other reporter types, and Purchased Transportation service, do not appear in this file.1last year
- last year
- Data collected on the SS-30 form. Transit agencies report to the NTD security personnel in terms of Full-Time Equivalents (FTE) according to the staffing levels at the beginning of the year. One FTE typically works 40 hours per week. An agency may use any reasonable method to allocate personnel across modes, such as allocating based on modal ridership or on modal annual trips. In certain instances, agencies may base personnel numbers on the prior year’s total hours worked.1last year
- This dataset details fuel mileage and gallons/kilowatt hours for each agency, mode, and type of service (TOS) as reported by agencies submitted data to the National Transit Database (NTD) for the 2022 and 2023 report years. This file is based on the 2022 and 2023 Energy Consumption database files available at https://transit.dot.gov/ntd/ntd-data Data Tables organize and summarize data from the 2022 and 2023 NTD in a manner that is more useful for quick reference and summary analysis. Only Full Reporters report energy consumption. Other reporter types do not appear in this dataset. Demand Response Taxi (DR/TX) mode and type of service combination does not report energy consumption and does not appear in this dataset. Finally, Non-dedicated fleets report energy consumption but not miles traveled. Thus for some agencies the given data for miles traveled are incomplete. Non-dedicated fleets represent about 7% of the data reflected in this dataset. In versions of the data tables from 2014-2021, you can find data on fuel and energy in the file called "Fuel and Energy" available from the NTD program website.1last year
- dataset of oceangoing, self-propelled, privately-owned U.S.-flag vessels of 1,000 gross tons and above that carry cargo from port to port for commercial and government customers.1last year
- Contains all Basic Mobility Control Message (BMCMs) generated during the Advanced Messaging Concept Development (AMCD) field testing program. While there is no specific standard in existence that addresses the content of a BMCM, the following format was derived to control the configuration and content of BMMs requested from the vehicle. All BMCMs are generated by the VCC Cloud server and transmitted to OBU clients through either a DSRC or cellular communications channel.1last year
- last year
- The FRA Milepost is a spatial file that originates of multiple sources and contains point locations of mileposts along the FRA's rail network. The mileposts was developed from varies sources and should only be used as a reference file. The railroad lines and their mileposts are privately owned and are subjected of changed based on the rail owner. If used for identifying specific locations, please contact the railroad to verify the mileposts numbers and their locations.1last year
- last year
- last year
- Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2IMCT-1 Verify V2I communication for log file offload.1last year
- 1last year
- The FMCSA Safety Measurement System (SMS) data, consists of active Intrastate Non-Hazmat Motor Carriers of property and/or passengers. File is comma delimited. One carrier per row.1last year
- This dataset offers insight on weekly fluctuation of the gasoline product supply, which is an important part of any analysis of construction trends, materials and operating costs associated with highway repair and construction, and changes in traffic volume. These data come directly from the Energy Information Administration (EIA) website. The EIA publishes the average daily amount of gasoline supplied in barrels, which HPPI converts to an average number of gallons of gasoline per week.1last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 20061last year
- Contains all PVDs generated during the AMCD field testing program. The probe vehicle message is used to exchange status about a vehicle with other DSRC readers to allow the collection of information about a typical vehicle’s traveling behaviors along a segment of road. The exchanges of this message as well as the event which caused the collection of various elements defined in the messages are in Annex B of the SAE J2735 standard.1last year
- Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).1last year
- This dataset contains a sample of the broadcast Traveler Information Messages (TIM) being generated by the Wyoming Connected Vehicle (CV) Pilot. The full set of TIMs can be found in the ITS DataHub data sandbox. Revision Note: This dataset only contains TIM sample data prior to December 18, 2018. For the most recent sample of TIM data, please refer to the Schema Version 6 dataset or retrieve the data from the ITS DataHub data sandbox.1last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 20031last year
- Curated FRA Safety data pertaining to Rail Equipment Accidents (Form 54) Unique Train Accidents Please note that this dataset displays unique train accidents. When an accident involves multiple railroads, each railroad must report its data. As a result, there can be multiple records for one accident. This dataset has been modified to pull and display one record for each accident. Highway-rail crossing incidents have also been removed from this dataset because they are not considered train accidents. To see the full dataset with all reports with all data for all accidents, please visit https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj1last year
- State, County and City FIPS (Federal Information Processing Standards) codes are a set of numeric designations given to state, cities and counties by the U.S. federal government. All geographic data submitted to the FRA must have a FIPS code.1last year
- last year
- The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).1last year
- *Dataset* Records showing the history of each authority granted to a carrier/broker/freight forwarder, along with the dates of the original authority action (e.g., “granted”) and the final authority action (e.g., “revoked”). The dataset contains the DOT number and docket number of the entity that holds or held the authority. As there can be multiple authorities for a single entity, there may be multiple records for an entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- 2015 Traffic Volume Data - FHWA's TMAS Data Program (based on unweighted raw continuous traffic count information collected by state highway agencies)1last year
- *Dataset* Information on the implementation dates of an active or pending insurance policy (posted date, effective date and cancel effective date). In addition to these dates, the record contains the insurance company name, the BI&PD underlying limit and maximum limit amounts, and the DOT number and docket number of the carrier/broker/freight forwarder that holds the policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- *Dataset* Records for carrier/broker/freight forwarder active or pending individual insurance policies. The records are linked to the entities by docket numbers included in the dataset. The dataset contains information on the insurance policy, including insurance company name, policy number and type of insurance. Entities can hold multiple insurance policies, so there may be multiple records associated with a particular entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- This dataset details vehicle types and ages for each transit agency reporting to the NTD in the 2022 and 2023 report years. Non-dedicated fleets do not report Year of Manufacture and are thus excluded from the Age Distribution table. Agencies do not report Useful Life Benchmark for non-dedicated fleets or fleets for which the agency does not have capital replacement responsibility. These fleets are excluded from calculations of the percentage of vehicles meeting or exceeding their useful life. In versions of the data tables from before 2014, you can find data on vehicles in the file called "Age Distribution of Active Vehicle Inventory." In years 2014-2021, you can find this data in the "Vehicles" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- *Dataset* Information on insurance forms that were rejected by FMCSA. The dataset contains information on the insurance policy associated with the form, along with the date that the form was rejected and the reason for rejection (e.g., “Policy is already cancelled”). The dataset contains the DOT number and docket number of the carrier/broker/freight forwarder associated with the policy. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- The National Bicycle Network is a geospatial dataset for nationwide bicycle routes. It is based on data and information released by public agencies such as state transportation departments, local Metropolitan Planning Organizations, local Councils of Government, city, and county public works and transportation departments. The FHWA Office of Highway Policy Information (HPPI) integrates all releases into one nationwide bicycle network, construction, and operating of such facilities as a safe, efficient, and equitable travel mode.1last year
- Test Data of Proof-of-Concept Vehicle Platooning Based on Cooperative Adaptive Cruise Control (CACC)The data represent the performance of a proof-of-concept vehicle platooning based on the Cooperative Adaptive Cruise Control (CACC) application. The Federal Highway Administration’s Turner Fairbank Highway Research Center (TFHRC), in conjunction with the Volpe National Transportation Systems Center, tested and evaluated this prototype system in 2016. Researchers in the Saxton Transportation Operations Laboratory at TFHRC designed and built the Cooperative Automated Research Mobility Applications (CARMA) platform version 1 that enables the implementation of the proof-of-concept CACC-based platooning in passenger vehicles equipped with production adaptive cruise control, and vehicle-to-vehicle communications using dedicated short-range communications (DSRC). The data characterize the state-of-the-art capability of the CACC application based on engineering tests that were performed on closed tracks by professional drivers and using prescribed test procedures. The test data are separated into sets that correspond to test date and time, and test run number. The data include performance parameters that were collected from the CACC application and data acquisition systems, including vehicle controller area network data, CARMA's MicroAutoBox, DSRC radios, and an independent measurement system. The tests were conducted at US Army’s Aberdeen Test Center located at Aberdeen Proving Grounds, MD. Further documentation can be found here: https://rosap.ntl.bts.gov/view/dot/1038.1last year
- This dataset details service and cost efficiency metrics for agencies reporting to the National Transit Database in the 2022 and 2023 report years. Only Full Reporters report data on Passenger Miles. The columns containing ratios have been calculated as the average across all reporting modes, not as the ratio of summed data. Thus, each transit agency received equal weight, regardless of that agency's total ridership. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Federal Funding Allocation, Operating Expenses, and Service database files. In years 2015-2021, you can find this data in the "Metrics" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. In versions of the NTD data tables from before 2014, you can find data on metrics in the files called "Fare per Passenger and Recovery Ratio" and "Service Supplied and Consumed Ratios." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- Historic Highway Performance Monitoring System sample data for the year 20071last year
- Identifies the study field and study results that arise from ad hoc examination of items, usually inspected in support of a particular study or verification/refutation of a specific trend. This inspection type is a Level IV inspection.1last year
- last year
- Beginning in 2023, certain agencies are required to submit one week of service data on a monthly basis to comply with FTA’s Weekly Reference reporting requirement on form WE-20. This data release will therefore present the limited set of key indicators reported by transit agencies on this form and will be updated each month with the most current data. The resulting dataset provides data users with data shortly after the transit service was provided and consumed, over one month in advance of FTA’s routine update to the Monthly Ridership Time Series dataset. One use of this data is for reference in understanding ridership patterns (e.g., to develop to a full month estimate ahead of when the data reflecting the given month of service is released by FTA at the end of the following month). Generally, FTA has defined the reference week to be the second or third full week of the month. All sampled agencies will report data referencing the same reference week. The form collects the following service data points, as described in the metadata below: • Weekday 5-day UPT total for the reference week; • Weekday 5-day VRM total for the reference week; • Weekend 2-day UPT total for either the weekend preceding or following the reference week; and • Weekend 2-day VRM total for either the weekend preceding or following the reference week. • Vehicles Operated in Maximum Service (vanpool mode only) for the reference week. FTA has also derived the change from the prior month for the same agency/mode/type of service/data point. Users should take caution when aggregating this measure and are encouraged to use the dataset export to measure service trends at a higher level (i.e., by reporter or nationally). For any questions regarding this dataset, please contact the NTD helpdesk at ntdhelp@dot.gov .1last year
- Historic Highway Performance Monitoring System sample data for the year 20041last year
- *Dataset* Records for each BOC3 agent hired by a carrier/broker/freight forwarder. Each entity must hire a BOC3 agent to represent them in legal matters to obtain operating authority. In some cases, entities may act as their own BOC3 agent. The records in the dataset contain the BOC3 agent’s name and address. The dataset also contains the DOT number and docket number of the represented entity. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Signal Phasing and Timing Message (SPaT) Messages transmitted by road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, SPaT data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow SAE J2735 data frames (Section 6) and structure (Section 7). This dataset holds a flattened sample of the SPaT data from Tampa CV Pilot. A column of random numbers (randomNum) was added to allow for random sampling of data points within Socrata.1last year
- The FMCSA Safety Measurement System (SMS) data, consists of summary results of all active Interstate and Intrastate Hazmat Motor Carriers of passengers only. File is comma delimited.1last year
- This dataset details vehicle types and ages for transit agencies reporting to the National Transit Database in the 2022 and 2023 report years. Vehicle types describe the vehicles employed in direct operation or support of transit service. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Revenue Vehicle Inventory and Service Vehicle Inventory database files. Rural reporters that operate in more than one state report their vehicles in only one of their states. In years 2015-2021, you can find this data in the "Vehicles" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- last year
- Historic Highway Performance Monitoring System sample data for the year 20081last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.1last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data1last year
- last year
- *Dataset* Information on carrier/broker/freight forwarder authorities that have been revoked by FMCSA. The dataset includes the DOT number and docket number of the entity, the type of authority revoked, and the reason. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information.1last year
- Historic Highway Performance Monitoring System sample data for the year 20091last year
- Part of the Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Verify that OBUs use different LTE-V2X Configuration Profiles based on the vehicle's speed. Host and remote vehicles travelling below 120 kmph Host and remote vehicles travelling above 120 kmph1last year
- Licensed driver data from Highway Statistics table DL-22, broken down by state, sex, and age group.1last year
- During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data1last year
- The Tampa CV Pilot generates data from the interaction between vehicles and between vehicles and infrastructure. This dataset consists of Traveler Information Messages (TIMs) transmitted by road-side units (RSU) located throughout the Tampa CV Pilot Study area. The full set of raw, TIM data from Tampa CV Pilot can be found in the ITS Sandbox. The data fields follow a SAE J2735 TIM message structure to convey important traffic information to onboard units (OBU) of equipped vehicles. Refer to SAE J2735 Section 5.16 Message: MSG_TravelerInformation Message (TIM). This dataset holds a flattened sample of the TIM data from Tampa CV Pilot. Three additional fields were added to this Socrata dataset during ETL: a geo column (travelerdataframe_msgId_position) to allow for mapping of the geocoded TIM data within Socrata, a random number column (randomNum) to allow for random sampling of data points within Socrata, and a time of day generated column (metadata_generatedAt_timeOfDay) to allow for filtering of data by generated time.1last year
- This dataset details service schedules for Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Counties and Places: https://data.transportation.gov/Public-Transit/Demand-Response-Geographic-Area-Coverage-Counties-/3kum-6vpd/about_data Passenger Eligibility and Requirements: https://data.transportation.gov/dataset/Demand-Response-Geographic-Area-Coverage-Passenger/h9qc-expu/about_data If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- The FMCSA Safety Measurement System (SMS) data, consists of summary results of all active Interstate and Intrastate Hazmat Motor File Description: Carriers of property and/or passengers. File is comma delimited. One carrier per row.1last year
- This dataset shows Amtrak stations in opportunity zones1last year
- This dataset contains a sample of the broadcast Traveler Information Messages (TIM) being generated by the Wyoming Connected Vehicle (CV) Pilot. This dataset only contains SchemaVersion 6 TIM sample data from December 18, 2018 to present. It is updated hourly and will hold up to 3 million of the most recent TIM records. The Schema Version 6 data is described further here. For sample TIM data prior to December 18, 2018, please refer to the Schema Version 5 dataset. The full set of TIMs can be found in the ITS Sandbox.1last year
- Identifies the type, make, company number, license plate, license plate state, VIN, CVSA Decal, and CVSA Number. There can be multiple Inspection Units per inspection.1last year
- Provides detailed fare information for highest and lowest fare markets under 750 miles. For a more complete explanation, please read the introductory information at the beginning of Table 5 itself in the report (https://www.transportation.gov/office-policy/aviation-policy/domestic-airline-consumer-airfare-report-pdf).1last year
- The report includes inspections involving violations of the FMCSR or HRM.1last year
- Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation.The Signal Plans for Roadside Equipment (RSE) data contains the basics of a Signal Phase and Timing (SPAT) message. This data includes SPAT message and the timestamp of the SPAT message. The data also provides the signal phase and timing information for one or more movements at an intersection.1last year
- The FMCSA New Entrant Safety Assurance Program out of service (OOS) data, consists of all entities that have received an OOS order from FMCSA. File is comma delimited.1last year
- This dataset details station/facility types and counts for each applicable agency reported to the National Transit Database for report years 2022 and 2023. NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Facilities and Transit Stations database files. In years 2015-2021, you can find this data in the "Facilities and Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.1last year
- last year
- last year
- The FMCSA Crash File contains data from state police crash reports involving drivers and vehicles of motor carriers operating in the U.S. Each report contains about 80 data elements pertaining to the motor carrier, driver, vehicles, and circumstances of a crash. Due to sensitive and/or privacy restrictions, driver, and hazardous materials data are not included in any crash files released to the public. The Crash File may contain multiple records for a crash. Separate reports are entered for each commercial motor vehicle involved in a crash. These multiple reports can be distinguished by the Crash Report Number field.1last year
Title
Updated