Road edges are defined as the edge of the improved surface including the improved shoulder but do not include the unimproved shoulder, only the travel part of the road. The road network is compiled to include all open intersections. Features do not overlap sidewalks, but have the sidewalk area cut out of the road polygons. Overlapping features are acceptable if one of the features is hidden. Road: A generally named thoroughfare, that is usually paved and can be public or private. Unimproved thoroughfares are excluded. Road polygons are formed by a combination of road edge, curb, sidewalk, street intersection closure line, and map sheet edge. Paved Median Island: Perimeter of non-traffic paved areas that separate traffic lanes in opposing directions. Unpaved Median Island: Perimeter of non-traffic grassy, unpaved areas that separate traffic lanes in opposing directions. Paved Traffic Island: Perimeter of non-traffic concrete areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Unpaved Traffic Island: Perimeter of non-traffic unpaved, grassy areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Alley: Perimeter of alleys first plotted photogrammetrically from other indicators such as building footprints, fence lines, curb lines, walls, paved or unpaved drives, and map sheet edge. Alley polygons are closed along the lines where they intersect with road polygons. Paved Drive: A paved driveway for a building or entranceway for a parking lot. Driveways are neither streets nor alleys, but provide access to public facilities, such as a drive to a monument, museum, hotel, large estate, sports field or golf course, grounds of the U.S. Capitol, etc. If a driveway is less than 200 feet and leads to a parking lot, the entire paved area is captured as Parking Lot. Driveways are photogrammetrically compiled as polygons and not compiled from individual vectors on different levels. Parking Lot: Generally paved surfaces used for cars to park on. Paved drives usually form entrances to these features, if the drive is more than 200 feet. If the driveway is less than 200 feet leading into the parking lot, the entire paved area is captured as Parking Lot. Parking lots sharing a common boundary with linear features must have the common segment captured once, but coded as both polygon and line. Small parking areas, where individuals park their cars in the middle of a block off a public alley, are not captured as parking lots. These are either public space (e.g., alleys) or private space where owners permit parking to occur. Intersection: A location where more than one road comes together. For standard cross streets, intersection polygons are bounded by curbs and four closure lines at street intersection crosswalks (outer line) or placed arbitrarily where crosswalks could logically be placed. For "T" intersections, the polygons are bounded by curbs and three such closure lines. Complex intersections can have more closure lines. Entire traffic circles are coded as intersections. Hidden Road: A section of a road that passes underneath a bridge or overpass and is not visible in an aerial photograph, but the location can be interpreted based on the road on either side of the bridge. Hidden Median: A road median that exists underneath a bridge or overpass and is not fully visible in an aerial photograph, but the location can be interpreted based on the information visible on either side of the bridge.

Road Segmentation Dataset

This dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene.

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The dataset can be utilized for… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/roads-segmentation-dataset.

The TIGER/Line Roads County-based dataset was released August 08, 2024, by the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529082

The US Car Accidents (2016–2023) dataset provides a comprehensive record of road accidents across the United States over an eight-year period. It is designed for data scientists, students, and researchers who want to analyze accident patterns, predict risks, explore factors affecting road safety, and build machine learning models for accident severity prediction.

This dataset includes detailed information on accident location, environment, weather, roadway conditions, traffic influence, and severity levels. It can be used for time-series analysis, geospatial studies, classification, regression, or exploratory data analysis (EDA).

This dataset reports the historical National Highway System 50th percentile median speeds for various roadway types, months, and vehicles on US roads. Data collected by Department of Transportation.

Tabular data includes: - Year - Month - Vehicle Type - Time Period (AM/PM) - Area Type - Functional Classification - Speed: MPH

Motor Trend Car Road TestsDescriptionThe data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).FormatA data frame with 32 observations on 11 variables.1 - mpg: Miles/(US) gallon2 - cyl: Number of cylinders3 - disp: Displacement (cu.in.)4 - hp: Gross horsepower5 - drat: Rear axle ratio6 - wt: Weight (1000 lbs)7 - qsec: 1/4 mile time8 - vs: Engine shape (0 = v-shaped, 1 = straight)9 - am: Transmission (0 = automatic, 1 = manual)10 - gear: Number of forward gears11 - carb: Number of carburettorsSourceHenderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

Urban SDK is a GIS data management platform and global provider of mobility, urban characteristics, and alt datasets. Urban SDK Traffic data provides traffic volume, average speed, average travel time and congestion for logistics, transportation planning, traffic monitoring, routing and urban planning. Traffic data is generated from cars, trucks and mobile devices for major road networks in US and Canada.

"With the old data I used, it took me 3-4 weeks to create a presentation. I will be able to do 3-4x the work with your Urban SDK traffic data."

Traffic Volume, Speed and Congestion Data Type Profile:

• Traffic volume in annual average daily and daily traffic volumes per roadway
• Average travel speed in 15 minute and hourly intervals per roadway
• Travel time in seconds in 15 minute intervals per roadway
• Commute travel time in minutes in annual interval estimates in geohash boundaries
• Congested roadway segments based on travel time reliability in monthly intervals per roadway
• Traffic data attributed spatially to state, county, road functional class, road name, road segment, segment length in km or miles as geojson

Industry Solutions include:

• Transportation Planning
• Traffic Monitoring
• Congestion Management and Trend Analysis
• Travel Demand Modeling
• Traffic Impact Analysis
• Parking Analysis
• Transit System Planning
• Route Planning
• Civil Engineering
• Site Selection

Use cases:

• Traffic monitoring, data analysis, and forecasting for transportation, transit, and urban planning.
• Improve dynamic routing with accurate travel time and congestion data
• Environmental and emissions analysis
• Travel demand and transportation modeling
• Location analysis and assessment for commercial site selection for retail or logistics related locations

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.

Vehicle Detection Dataset

Dataset comprises 5,000 high-resolution (≥1080p) videos of Serbian road traffic captured from bridge vantage points, accompanied by annotated frame images with JSON bounding box annotations. It is designed for research in vehicle detection and classification, focusing on analyzing traffic flows and traffic density to support AI-driven traffic management solutions.

By utilizing this dataset, researchers and developers can enhance their capabilities in traffic prediction and develop systems to optimize road networks and manage daily traffic effectively.- Get the data

The dataset provides detailed visual data of vehicles (cars and minivans) in various traffic conditions, supporting the development of robust AI models for traffic monitoring and analysis.

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Researchers can utilize this dataset to explore safety analysis methodologies and develop deep learning models for traffic video analysis and traffic prediction that aim to improve urban mobility and prevent traffic incidents.

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Description

This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset currently contains approximately 7.7 million accident records. For more information about this dataset, please visit here.

Acknowledgements

If you use this dataset, please kindly cite the following papers:

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. "Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights." In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.

Content

This dataset was collected in real-time using multiple Traffic APIs. It contains accident data collected from February 2016 to March 2023 for the Contiguous United States. For more details about this dataset, please visit [here].

Inspiration

The US-Accidents dataset can be used for numerous applications, such as real-time car accident prediction, studying car accident hotspot locations, casualty analysis, extracting cause and effect rules to predict car accidents, and studying the impact of precipitation or other environmental stimuli on accident occurrence. The most recent release of the dataset can also be useful for studying the impact of COVID-19 on traffic behavior and accidents.

Sampled Data (New!)

For those requiring a smaller, more manageable dataset, a sampled version is available which includes 500,000 accidents. This sample is extracted from the original dataset for easier handling and analysis.

Other Details

Please note that the dataset may be missing data for certain days, which could be due to network connectivity issues during data collection. Regrettably, the dataset will no longer be updated, and this version should be considered the latest.

Usage Policy and Legal Disclaimer

This dataset is being distributed solely for research purposes under the Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By downloading the dataset, you agree to use it only for non-commercial, research, or academic applications. If you use this dataset, it is necessary to cite the papers mentioned above.

Inquiries or need help?

For any inquiries or assistance, please contact Sobhan Moosavi at sobhan.mehr84@gmail.com

This dataset shows counts of transactions associated with authorizing vehicles to be used on public roads, commonly referred to as “buying tabs” or “buying tags”. The data includes registration activity by fuel type, county, primary use class, and date. This is comparable to the Fee Distribution Report #13, that is titled "Motor Vehicle Registration By Class and County".