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 10 of 10 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 data1Licence not specified22 days ago
- 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.1Licence not specified22 days ago
- 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.)1Licence not specified22 days ago
- 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.1Licence not specified22 days ago
- 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 data1Licence not specified22 days ago
- 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.1Licence not specified22 days ago
- Historic Highway Performance Monitoring System sample data for the year 19991Licence not specified22 days ago
- Historic Highway Performance Monitoring System sample data for the year 19901Licence not specified22 days ago
- Historic Highway Performance Monitoring System sample data for the year 19941Licence not specified22 days ago
- Historic Highway Performance Monitoring System universe data for the year 19851Licence not specified22 days ago
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