AndreaBozzo/ceres-open-data-index huggingface dataset:
Tampa CV Pilot Signal Phasing and Timing (SPaT) Sample

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 requested from the [ITS DataHub 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.
Tampa CV Pilot Basic Safety Message (BSM) Sample

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 requested from the [ITS DataHub 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.
Synthesize Data for Emerging Data Cleaning and Fusion Tool Zone Name to Link

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file maps probe OD zone names from vendor to network links
Synthesize Data for Emerging Data Cleaning and Fusion Tool Waypoint

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file includes timestamped coordinates associated with anonymized trips.
Synthesize Data for Emerging Data Cleaning and Fusion Tool Trajectories

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file contains detailed trip path information, including time, location, speed, and segment ID.
Synthesize Data for Emerging Data Cleaning and Fusion Tool TMC to Link

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file maps TMC segments to network links.
Synthesize Data for Emerging Data Cleaning and Fusion Tool TMC Identification

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file provides metadata for each TMC, including road name, direction, and spatial extent.
Synthesize Data for Emerging Data Cleaning and Fusion Tool SegmentId to Link

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file links vendor-specific segment identifiers (SegmentId in trip path data) to network links.
Synthesize Data for Emerging Data Cleaning and Fusion Tool Readings

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file includes timestamped speed measurements associated with each TMC segment.
Synthesize Data for Emerging Data Cleaning and Fusion Tool Node

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file defines the network's nodes in GMNS format.
Synthesize Data for Emerging Data Cleaning and Fusion Tool Link

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file defines the network's links in GMNS format.
Synthesize Data for Emerging Data Cleaning and Fusion Tool Lane Readings

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file contains timestamped lane-level volume and occupancy readings.
Synthesize Data for Emerging Data Cleaning and Fusion Tool Inventory

The Synthesize Data for Emerging Data Cleaning and Fusion Tool dataset is a privacy-preserving synthetic dataset developed to support and demonstrate the functionalities of the Emerging Data Cleaning and Fusion Tool. It replicates the general structure and statistical characteristics of real-world multimodal traffic data while ensuring that no sensitive or identifiable information is included. The dataset integrates multiple data sources, including TMC data, sensor data, waypoint data, trip path data, and probe OD data. It is designed to facilitate testing, validation, and demonstration of data cleaning, fusion, and calibration processes without relying on proprietary datasets. All data elements have been anonymized and perturbed through controlled noise-injection mechanisms to preserve analytical utility while maintaining strong privacy protection. This work is funded by the Federal Highway Administration (FHWA) under the project titled Emerging Data Cleaning and Fusion for Traffic Model Calibration, as part of ongoing efforts to promote the effective use of emerging data sources in transportation modeling and planning. This file contains sensor metadata, such as location, direction, and road segment information.
State Motor Fuel Tax Receipts (MF-201)

Annual adjusted net total State motor fuel tax receipts from the Highway Statistics Series table MF-210. The data source is Table MF-1. Increases in 2011 and 2012 were due in large part to a change in California's method of taxing fuel. 2014 saw various States increase motor fuel taxes (California, Connecticut, and Wyoming), and 2015 saw more fuel use.
SSM from UDOT and Panasonic Connected Vehicle Data Ecosystem Program

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted 7 days of Signal Status Message (SSM) in J2735 standards from 3 intersections in Orem, UT. This includes: (1) vehicles equipped with OBUs (Onboard Units) for real-time V2X (Vehicle-to-Everything) data transmission and reception, (2) road-side units (RSUs) capable of capturing, transmitting, and processing data from the transportation environment, and (3) a secure, scalable cloud platform for aggregating, analyzing, and visualizing transportation data.
SRM from UDOT and Panasonic Connected Vehicle Data Ecosystem Program

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted 7 days of deduplicated Signal Request Messages (SRMs) in J2735 standards from 3 intersections in Orem, UT. This includes: (1) vehicles equipped with OBUs (Onboard Units) for real-time V2X (Vehicle-to-Everything) data transmission and reception, (2) road-side units (RSUs) capable of capturing, transmitting, and processing data from the transportation environment, and (3) a secure, scalable cloud platform for aggregating, analyzing, and visualizing transportation data.
SPaT from UDOT and Panasonic Connected Vehicle Data Ecosystem Program

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted 7 days of Signal Phase and Timing Messages (SPaT) in J2735 standards from 3 intersections in Orem, UT. This includes: (1) vehicles equipped with OBUs (Onboard Units) for real-time V2X (Vehicle-to-Everything) data transmission and reception, (2) road-side units (RSUs) capable of capturing, transmitting, and processing data from the transportation environment, and (3) a secure, scalable cloud platform for aggregating, analyzing, and visualizing transportation data.
SMS Input - Motor Carrier Census Information

This data is from an FMCSA Comprehensive Safety Analysis (CSA) monthly data run, which contains the FMCSA registration data of all active Interstate and Intrastate Hazmat Motor Carriers of property and/or passengers. Contains the USDOT Number, company names, addresses, contacts, telephone and fax numbers, e-mail, HazMat flag, passenger carrier flag, number of power units, number of drivers, mileage, mileage year, operation, and classification registration information. File is comma delimited. One carrier per row.
Security Event Rates, Top 25 UZAs

This dataset details unlinked passenger trips/vehicle revenue miles (UPT/VRM) for 5 different metrics: Major Passenger Assaults and Homicides, Major Worker Assaults and Homicides, Fatalities (safety), Fatalities (security), Fatalities (trespasser/suicide). It breaks the metrics out by mode (bus or rail) for each month going back to January 2023 in the top 25 largest urbanized areas (UZAs) (based on population). It also has a national-level for each metric, so individual UZAs or groups of UZAs can be compared against the national trend.
Road Weather Demonstration Data

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.
Revocation - All With History

*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. 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.
Rejected - All With History

*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. 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.
Recalls Data

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.
Railroad Equipment Accident/Incident Source Data (Form 54)

Rail equipment accidents/incidents, collisions, derailments, fires, explosions, acts of God, or other events involving the operation of railroad on-track equipment (standing or moving) and causing reportable damages greater than the reporting threshold for the year in which the accident/incident occurred, must be reported by railroads to the FRA on Form FRA 6180.54 - Rail Equipment Accident/Incident. This dataset is the source dataset and contains raw data values. It replaced the legacy data download (https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/on_the_fly_download.aspx). 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. The data dictionary can be found here: https://datahub.transportation.gov/api/views/aqxq-n5hy/files/ea00a728-94b0-43e7-8c11-8481f13170a7?download=true&filename=accfile_EFFECTIVE_060111%20(8).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/.
Rail Mileposts

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.