AndreaBozzo/ceres-open-data-index huggingface dataset:
Estimated Lane-Miles of Public Roads by Functional System and State,  1980 - 2024 (HM-60)

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.)
Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Western Massachusetts (Runs)

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).
Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Western Massachusetts (Radar Points)

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).
Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Western Massachusetts (Instances)

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).
Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Northern Virginia (Runs)

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).
Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Northern Virginia (Radar Points)

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).
Enhancing Microsimulation Models for Improved Work Zone Planning: Car-Following Data from Northern Virginia (Instances)

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).
Enhanced Transit Safety Retrofit Package (E-TRP)

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.
Department of Transportation Inventory of Artificial Intelligence Use Cases

This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases. The Advancing American AI Act, Executive Order (EO) 13960, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government, and Office of Management and Budget (OMB) Memorandum M-25-21, Accelerating Federal Use of AI through Innovation, Governance, and Public Trust, require Federal agencies to report on their use of artificial intelligence (AI).
Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents Matched

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.
Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019

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 data
Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Processed Integrated Traffic Work Zone Data

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.
Crossing Inventory Source Data (Form 71) - Historical

A highway-rail crossing is a location where a public highway, road, street, or private roadway, including associated sidewalks and pathways, crosses one or more railroad tracks. All such crossings are reported by railroads and state Department of Transportations to the FRA on Form FRA F 6180.71 U.S. DOT Crossing Inventory Form. Each location has an assigned U.S. DOT Grade Crossing Identification Number, or crossing ID. This dataset is the source dataset and contains raw data values. It replaced the legacy data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/DownloadCrossingInventoryData.aspx). To download data that contains data in a user-friendly human-readable format, please reference https://data.transportation.gov/Railroads/Crossing-Inventory-Data-Form-71-Historical/vhwz-raag/about_data. The data dictionary can be found here: https://data.transportation.gov/api/views/8uv2-y4is/files/9ad38db9-c214-4e51-8e0a-31eeaca0d77d?download=true&filename=GCIS_Data_Dictionary_External_Use_v3-2-0-152_Rel_06-21-2022.pdf For information on how to filter and export data, please visit: https://data.transportation.gov/stories/s/Download-Export-and-Print-User-Guide/s8hj-vns8/. To view the data release schedule, please visit: https://data.transportation.gov/stories/s/Data-Release-Schedule/qfc9-tapk/.
Crossing Inventory Source Data (Form 71) - Current

A highway-rail crossing is a location where a public highway, road, street, or private roadway, including associated sidewalks and pathways, crosses one or more railroad tracks. All such crossings are reported by railroads and state Department of Transportations to the FRA on Form FRA F 6180.71 U.S. DOT Crossing Inventory Form. Each location has an assigned U.S. DOT Grade Crossing Identification Number, or crossing ID. This dataset is the source dataset and contains raw data values. It replaced the legacy data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/DownloadCrossingInventoryData.aspx). 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. The data dictionary can be found here: https://data.transportation.gov/api/views/8uv2-y4is/files/9ad38db9-c214-4e51-8e0a-31eeaca0d77d?download=true&filename=GCIS_Data_Dictionary_External_Use_v3-2-0-152_Rel_06-21-2022.pdf For information on how to filter and export data, please visit: https://data.transportation.gov/stories/s/Download-Export-and-Print-User-Guide/s8hj-vns8/. To view the data release schedule, please visit: https://data.transportation.gov/stories/s/Data-Release-Schedule/qfc9-tapk/.
Carrier - All With History

*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). Note: This dataset was last refreshed on 05/14/2026 and will no longer be updated. See attached "FMCSA Operating Authority Data Dictionary_V2" for more information.
Crossing Inventory Data (Form 71) - Historical

A highway-rail crossing is a location where a public highway, road, street, or private roadway, including associated sidewalks and pathways, crosses one or more railroad tracks. All such crossings are reported by railroads and state Department of Transportations to the FRA on Form FRA F 6180.71 U.S. DOT Crossing Inventory Form. Each location has an assigned U.S. DOT Grade Crossing Identification Number, or crossing ID. 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. The data dictionary can be found here: https://data.transportation.gov/api/views/m2f8-22s6/files/55a89796-f127-402c-82e3-09d5af892b42?download=true&filename=Form71_Data_Dictionary.xlsx. For information on how to filter and export data, please visit: https://data.transportation.gov/stories/s/Download-Export-and-Print-User-Guide/s8hj-vns8/. To view the data release schedule, please visit: https://data.transportation.gov/stories/s/Data-Release-Schedule/qfc9-tapk/.
Crossing Inventory Data (Form 71) - Current

A highway-rail crossing is a location where a public highway, road, street, or private roadway, including associated sidewalks and pathways, crosses one or more railroad tracks. All such crossings are reported by railroads and state Department of Transportations to the FRA on Form FRA F 6180.71 U.S. DOT Crossing Inventory Form. Each location has an assigned U.S. DOT Grade Crossing Identification Number, or crossing ID. 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. The data dictionary can be found here: https://data.transportation.gov/api/views/m2f8-22s6/files/55a89796-f127-402c-82e3-09d5af892b42?download=true&filename=Form71_Data_Dictionary.xlsx. For information on how to filter and export data, please visit: https://data.transportation.gov/stories/s/Download-Export-and-Print-User-Guide/s8hj-vns8/. To view the data release schedule, please visit: https://data.transportation.gov/stories/s/Data-Release-Schedule/qfc9-tapk/.
Crash File

The FMCSA Crash File contains 59 data elements comprised of data received 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 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.
Consumer Airfare Report: Table 7 - Fare Premiums for Select Cities with More Than 20 Passengers per Day

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-reports
Consumer Airfare Report: Table 6 - Contiguous State City-Pair Markets That Average At Least 10 Passengers Per Day

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-reports
Consumer Airfare Report: Table 2 - Top 1,000 City-Pair Markets

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-reports
Consumer Airfare Report: Table 1a - All U.S. Airport Pair Markets

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-reports
Complete Monthly Ridership (with Adjustments and Estimates)

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.
Company Census File

The Company Census File contains records for active, inactive, and pending entities registered with FMCSA. 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.
DOT Consolidated Artificial Intelligence Use Case Inventory

This dataset is a list of Department of Transportation (DOT) Consolidated Artificial Intelligence (AI) use cases. The Advancing American AI Act, Executive Order (EO) 13960, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government, and Office of Management and Budget (OMB) Memorandum M-25-21, Accelerating Federal Use of AI through Innovation, Governance, and Public Trust, require Federal agencies to report on their use of artificial intelligence (AI).
Wyoming CV Pilot Traveler Information Message Sample (Schema Version 5)

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 requested through 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].