The Open Data Hub has numerous data relating to roads and speed, whether it be the Sydney Region Carriageway, the Tolls on our NSW roads, or the speed zones and speed camera locations.

Below you will find a full list of available data sets;

• NorthConnex
• Sydney Region Carriageway
• Road Segment Data from Data.NSW
• NSW Speed Cameras
• Speed Zones
• NSW Toll Road Data
• NSW Rest Areas
• Toll Calculator API
• Toll Calculator
• Historic Roads Travel Time Data (TTDS)
• NSW Roads Traffic Volume Counts API
• Data on the speeding fines issued by the speeding cameras and NSW Police
• Data on the speeding fines by the offender's postcode
• Traffic Lights Location
• Penalty Offences
• NSW Clearways
• Live Traffic Site Status
• Live Traffic Hazards
• Live Traffic Cameras

Speed limit information for most of Queensland's roads. Includes state and locally controlled roads. Point-in-time data as per date of collection in dataset.

Max Speed limit values in miles per hour. This data is an extract from the Geospatial Roadway Inventory Databse (GRID), which is TxDOT's system for managing roadway assets in Texas.Note: Extracts from GRID are made on a regular basis and reflect the state of the data at that moment. Assets on routes that are in the process of being edited may be affected.Update Frequency: 1 MonthsSource: Geospatial Roadway Inventory Database (GRID)Security Level: PublicOwned by TxDOT: TrueRelated LinksData Dictionary PDF [Generated 2025/04/24]

The road characteristics database (WKD) for speeds contains speed limits for all roads in the NWB. At the beginning of 2017, WKD was filled for the entire Netherlands with data supplied by municipalities. From that moment on, the new traffic decisions have been used via the Knowledge and Operation Center for Official Government Publications (KOOP) to detect and process changes in speed limits. The NWB changes faster than the speed limits are supplied by the road authorities or placed in COOP. Algorithms are used to supplement the speed where necessary on short intermediate road sections. As a result, the speed limit is unknown for a few percent. Since 2022, the features Trees, Entrances, Bowl Boundaries, Parking Points, Parking Spaces, Traffic Center, Traffic Types, Road Width, Road Categorization and Road Narrowings have been added to the database as a CSV file. NB: In residential areas where a maximum speed of 30 km per hour applies, or in a residential area, this leads to major deviations from reality. The number of rural roads with a 60 km limit has also increased significantly since 2017. The possible speeds that can be entered are 5, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 130 km per hour and unknown. The speeds only apply to roads that are open to car traffic. On cycle paths, footpaths and other roads that are not open to car traffic, the speed is entered as unknown. This also applies to the ferry connections. The file provides variable maximum speeds with a start time and an end time. These apply in particular to motorways. Outside this period with the indicated start time and end time, an alternative speed applies. So, for example, between 6:00 AM and 7:00 PM the speed limit is 100 km per hour and outside of that time the maximum speed is 120 km per hour. The road characteristics database for speeds also contains the recommended speed limits that apply to a specific road section or part thereof.

Speed zones are set to enable drivers travelling at a speed limit to safely respond to potential risks in the road environment. This dataset contains data for NSW speed zones that are categorised as: Ordinary Permanent Shared High Pedestrian School Variable Local Traffic Truck & bus Wet Weather School Bus Toll Plaza

Thanks to OS MasterMap Highways Network with Speed Data, you can also access road speed information through our product. It’s an additional dataset to help you plan logistics and monitor our roads more effectively.

Perhaps you're looking for more data about Great Britain's roads? Or maybe you're studying drive times or comparing vehicle types along routes or the impact of a new development? This could be the product for you.

We have enhanced our Highways Network family of products with these three speed data additions. Each product is supplied with an additional data file which will be either Average Speed, Speed Limits or a combination of both.

This map contains speed limits for all roads in the National Road Database (NWB).

Description from Rijkswaterstaat: "Since 2022, the features are Trees, Entrances, Bowl Boundaries, Parking Points , Parking spaces, Traffic center, Traffic types, Road width, Road categorization and Road narrowings added as a csv file to the database."

"The possible speeds that can be entered are 5, 15, 20, 30 40, 50, 60, 70, 80, 90, 100, 120, 130 km per hour, N/A and unknown. The speeds only apply to roads that are open to car traffic. On cycle paths , footpaths and other roads that are not open to car traffic, the speed is unknown. This also applies to the ferry connections. The file provides variable maximum speeds with a start time and an end time. These mainly apply to motorways. Outside this period with the indicated start time and end time, an alternative speed applies. So, for example, between 6:00 AM and 7:00 PM the speed limit is 100 km per hour and outside of that the maximum speed is 120 km per hour."

Traffic decisions, via the Knowledge and Operation Center for Official Government Publications (KOOP), are used to detect and process changes in speed limits.

Disclaimer:

A number of roads are currently still listed as "unknown" while the speed limit does not actually apply here (pedestrian paths and cycle paths, for example).

Source:

We keep track of speeds within a tool from the National Road Traffic Data Portal (NDW). You can view the map that the NDW offers via: https:// weghouden.ndw.nu/weghouden/wegvakken/323165013/bedrijven/maximumspeed. You can also download the data in shapefile format via https://opendata.ndw.nu/ .

To unlock the speeds within our Eindhoven Open Data portal we use a service from Rijkswaterstaat: https:// geo.rijkswaterstaat.nl/arcgis/rest/services/GDR/maximum_speeds_roads/FeatureServer/0

You can obtain more information and different publication formats from the Rijkswaterstaat data source via: https://maps.rijkswaterstaat.nl/dataregister-publicatie/srv/eng/catalog.search#/metadata/ d7df2888-0c0d-40f1-9b35-3c1a01234d01

Statewide Road Network including sealed and unsealed roads. The dataset represents navigable roads, including public and private access roads and tracks. A separate data layer stores 'unformed' DCDB centrelines which do not represent navigable roads. A limited number of associated features are stored separately as point features. Automatically updated when changes occur.

Roadway Posted Speed Limit Signs data consists of point feature geometry which represents the geographic location of posted speed limit signs along public roadways in the State of Maryland. PLEASE NOTE: This layer is now deprecated as of September, 2020 and will be removed in September 2021.Data has been replaced with the following https://maryland.maps.arcgis.com/home/item.html?id=7549d74e31df427a82a64ab5a19d74e3#overviewRoadway Posted Speed Limit Signs data is developed as part of the Highway Performance Monitoring System (HPMS) which maintains and reports transportation related information to the Federal Highway Administration (FHWA) on an annual basis. HPMS is maintained by the Maryland Department of Transportation State Highway Administration (MDOT SHA), under the Office of Planning and Preliminary Engineering (OPPE) Data Services Division (DSD). This data is used by various business units throughout MDOT, as well as many other Federal, State and local government agencies. Roadway Posted Speed Limit Signs data is key to understanding the location of posted speed limit signs throughout the State of Maryland. Roadway Posted Speed Limit Signs data is updated and published on an annual basis for the prior year. This data is for the year 2017.For additional information, contact the MDOT SHA Geospatial Technologies Email: GIS@mdot.state.md.us For additional information related to the Maryland Department of Transportation (MDOT) Website: https://www.mdot.maryland.gov/ For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA) Website: https://roads.maryland.gov/Home.aspx Data Legal Disclaimer: The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Link:https://geodata.md.gov/imap/rest/services/Transportation/MD_RoadwayPostedSpeedLimits/FeatureServer/0

Speed Signs is a spatial dataset (point) that shows the location of every Speed Limit sign across Victoria including advisory 'Speed Limit Ahead' signs. The data includes attributes such as Road Name, Sign Size, Type, Speed Value, Bearing and Direction. Variable speed signs display the alternative speed limit during the times that limit is active. This is captured in the variable time and day fields in the dataset. A standard ('static') speed sign with the prevailing speed limit is provided at the end of the zone which serves to end the variable zone, or act as a reminder sign for times when the variable speed limit isn't active. Data Quality Whilst every effort has been made to ensure this information is up-to-date, there may be instances where signs are not yet recorded in this system. Disclaimer No claim is made as to the accuracy or currency of the content on this site at any time. This data is provided on the basis that users undertake responsibility for assessing the relevance and accuracy of its content. The Victorian Government and Department of Transport and Planning accept no liability to any person or group for the data or advice (or the use of such data or advice) which is provided or incorporated into it by reference.

This map provides information on speed limits that are posted on state-maintained roadways in Virginia. Cities and towns set their own speed limits and these are not available to show on the map. Zoom in on the map to display the speed limits. Speed limits exist for all roads however; where this information is not available for mapping, they are not displayed. Most roads where speed limits are not shown are either rural, secondary roads (routes numbered 600 or greater) where a statutory 55 mph speed limit typically applies, or subdivision streets where a statutory 25 mph speed limit usually applies. These statutory speed limits are often are not posted on these roads. Click on any roadway to display the speed limit information.

This dynamic map service provodes a a linear representation of speed limit changes based on signs in the field or speed zones established by an Official Order.

Esri ArcGIS Online (AGOL) Hosted, View Feature Layer which provides access to the MDOT SHA Roadway Posted Speed Limit Signs data product.MDOT SHA Roadway Posted Speed Limit Signs data consists of point geometric features which represent the geographic locations of posted speed limit signs along MDOT SHA-maintained roadways throughout the State of Maryland. This layer is a hosted, view layer showing only Posted Speed Limit signage from the comprehensive MDOT SHA Roadway Sign Inventory. Roadway signs that share a sign support structure will be represented as stacked geometry.MDOT SHA Roadway Sign Inventory data is owned by the MDOT SHA Office of Traffic & Safety (OOTS). This data is currently updated on an annual basis. This is the latest version of the data, which was last updated in November 2019 (11/04/2019).MDOT SHA Roadway Sign Inventory data is published on ArcGIS Online for Maryland as a publicly available Hosted Feature Layer with Non-Restricted Access. Download / Export of the data is available in a variety of formats.For additional information, contact MDOT SHA OIT Enterprise Information Services:GIS@mdot.maryland.gov

Export the provincial highway speed enforcement signboard surface (police sign 52) facility data from the highway department's highway basic database.

This dataset contains data from the Sign Faces and Sign Assemblies layers. Signs are categorized by condition (Good, Fair, Poor) and Speed (Speed Limit and Speed Related). These datasets are Mandli data layers that were collected in the Summer of 2021 via LiDAR inventory.

For more information on Sign Faces, please see this Data Assessment Form. For more information on Sign Assemblies, please see this Data Assessment Form. For questions on the data please contact Scott Jones at wsjones@utah.gov. To download either of these data layers, please visit UDOT's Open Data Site.

This dataset contains annotated images of Polish roads, specifically curated for object detection tasks. The data was collected using a car camera on roads in Poland, primarily in Kraków. The images capture a diverse range of scenarios, including different road types and various lighting conditions (day and night).

Classes:

• Car (Vehicles without a trailer)
• Different-Traffic-Sign (Other traffic signs than warning and prohibition signs, mostly information and order signs)
• Green-Traffic-Light (Green traffic lights for cars only; green lights for pedestrians are not annotated)
• Motorcycle
• Pedestrian (People and cyclists)
• Pedestrian-Crossing (Pedestrian crossings)
• Prohibition-Sign (All prohibition signs)
• Red-Traffic-Light (Red traffic lights for cars only; lights for pedestrians are not annotated)
• Speed-Limit-Sign (Speed limit signs)
• Truck (Vehicles with a trailer)
• Warning-Sign (Warning signs)

Annotation Process:

Annotations were carried out using Roboflow. A total of 2,000 images were manually labeled, while an additional 9,000 images were generated through data augmentation. The labeled techniques applied were crop, saturation, brightness, and exposure adjustments.

Image Statistics Before Data Augmentation:

Approximately

• 400 cars per 100 photos
• 30 different-traffic-signs per photos
• 80 red-traffic-lights per photos
• 70 pedestrians per photos
• 50 warning signs per photos
• 50 pedestrian-crossings per photos
• 40 green-traffic-lights per photos
• 40 prohibition signs per photos
• 40 trucks per photos
• 20 speed-limit-signs per photos
• 2 motorcycles per photos

The photos were taken on both normal roads and highways, under various conditions, including day and night. All photos were initially 1920x1080 pixels. After cropping, some images may be slightly smaller. No preprocessing steps were applied to the photos.

Annotations are provided in YOLO format.

Image Statistics Before Data Augmentation:

Image Statistics After Data Augmentation:

Introduction

This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called BeamNG.tech. Virtual roads consist of sequences of road points interpolated using Cubic splines.

Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.

This dataset builds on top of our previous work in this area, including work on

test generation (e.g., AsFault, DeepJanus, and DeepHyperion) and the SBST CPS tool competition (SBST2021),

test selection: SDC-Scissor and related tool

test prioritization: automated test cases prioritization work for SDCs.

Dataset Overview

The TRAVEL dataset is available under the data folder and is organized as a set of experiments folders. Each of these folders is generated by running the test-generator (see below) and contains the configuration used for generating the data (experiment_description.csv), various statistics on generated tests (generation_stats.csv) and found faults (oob_stats.csv). Additionally, the folders contain the raw test cases generated and executed during each experiment (test..json).

The following sections describe what each of those files contains.

Experiment Description

The experiment_description.csv contains the settings used to generate the data, including:

Time budget. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.

The size of the map. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.

The test subject. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.

The test generator. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.

The speed limit. The maximum speed at which the driving agent under test can travel.

Out of Bound (OOB) tolerance. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.

Experiment Statistics

The generation_stats.csv contains statistics about the test generation, including:

Total number of generated tests. The number of tests generated during an experiment. This number is broken down into the number of valid tests and invalid tests. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.

Test outcome. The test outcome contains the number of passed tests, failed tests, and test in error. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.

The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (total oob) and its breakdown into OOB that happened while driving left or right. Further statistics about the diversity (i.e., sparseness) of the failures are also reported.

Test Cases and Executions

Each test..json contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.

The data about the test case definition include:

The road points. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (interpolated_points)

The test ID. The unique identifier of the test in the experiment.

Validity flag and explanation. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., the road contains sharp turns or the road self intersects)

The test data are organized according to the following JSON Schema and can be interpreted as RoadTest objects provided by the tests_generation.py module.

{ "type": "object", "properties": { "id": { "type": "integer" }, "is_valid": { "type": "boolean" }, "validation_message": { "type": "string" }, "road_points": { §\label{line:road-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "interpolated_points": { §\label{line:interpolated-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "test_outcome": { "type": "string" }, §\label{line:test-outcome}§ "description": { "type": "string" }, "execution_data": { "type": "array", "items": { "$ref" : "schemas/simulationdata" } } }, "required": [ "id", "is_valid", "validation_message", "road_points", "interpolated_points" ] }

Finally, the execution data contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).

The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the simulation_data.py module.

{ "$id": "schemas/simulationdata", "type": "object", "properties": { "timer" : { "type": "number" }, "pos" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel_kmh" : { "type": "number" }, "steering" : { "type": "number" }, "brake" : { "type": "number" }, "throttle" : { "type": "number" }, "is_oob" : { "type": "number" }, "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§ }, "required": [ "timer", "pos", "vel", "vel_kmh", "steering", "brake", "throttle", "is_oob", "oob_percentage" ] }

Dataset Content

The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (SDC-Scissor work and tool) and test prioritization (automated test cases prioritization work for SDCs).

SBST CPS Tool Competition Data

The data collected during the SBST CPS tool competition are stored inside data/competition.tar.gz. The file contains the test cases generated by Deeper, Frenetic, AdaFrenetic, and Swat, the open-source test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    DEFAULT
    200 × 200
    120
    5 (real time)
    0.95
    BeamNG.AI - 0.7


    SBST
    200 × 200
    70
    2 (real time)
    0.5
    BeamNG.AI - 0.7

Specifically, the TRAVEL dataset contains 8 repetitions for each of the above configurations for each test generator totaling 64 experiments.

SDC Scissor

With SDC-Scissor we collected data based on the Frenetic test generator. The data is stored inside data/sdc-scissor.tar.gz. The following table summarizes the used parameters.

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    SDC-SCISSOR
    200 × 200
    120
    16 (real time)
    0.5
    BeamNG.AI - 1.5

The dataset contains 9 experiments with the above configuration. For generating your own data with SDC-Scissor follow the instructions in its repository.

Dataset Statistics

Here is an overview of the TRAVEL dataset: generated tests, executed tests, and faults found by all the test generators grouped by experiment configuration. Some 25,845 test cases are generated by running 4 test generators 8 times in 2 configurations using the SBST CPS Tool Competition code pipeline (SBST in the table). We ran the test generators for 5 hours, allowing the ego-car a generous speed limit (120 Km/h) and defining a high OOB tolerance (i.e., 0.95), and we also ran the test generators using a smaller generation budget (i.e., 2 hours) and speed limit (i.e., 70 Km/h) while setting the OOB tolerance to a lower value (i.e., 0.85). We also collected some 5, 971 additional tests with SDC-Scissor (SDC-Scissor in the table) by running it 9 times for 16 hours using Frenetic as a test generator and defining a more realistic OOB tolerance (i.e., 0.50).

Generating new Data

Generating new data, i.e., test cases, can be done using the SBST CPS Tool Competition pipeline and the driving simulator BeamNG.tech.

Extensive instructions on how to install both software are reported inside the SBST CPS Tool Competition pipeline Documentation;

This service provides lines representing posted speed limits along centerlines of North Carolina state-maintained roads. This data comes from traffic ordinances governing speed limit; where there is no ordinance, the speed limit is 35 within municipalities and 55 outside. The N.C. Department of Transportation sets the speed limit for all state-maintained roads, including access-controlled highways, which are highways with medians that require drivers to enter or exit only at interchanges with bridges, inside the town or city limits. For other state-maintained roads within the municipal limits, NCDOT and the town or city must concur before changing the speed limit. Roads are designed for a specific speed. NCDOT may review the speed limit for various reasons, such as part of a study to improve highway safety, or for proposed new developments. Citizens and local officials may also request NCDOT to conduct a speed zone study to determine whether a road has the appropriate speed limits and signage.The department considers several factors when adjusting the speed limit, such as:Alignment of the roadwayTypes of development along the roadwayThe density, or number, of driveways on a corridorHow far one can see the roadCrash historyVarious speed dataOne of the most common types of speed data NCDOT uses is based on the speed at or below which 85 percent of drivers are traveling. NCDOT uses the 85th percentile to help avoid posting speed limits that are artificially low, which can become difficult to enforce. In the absence of strict enforcement, most people drive at the speed they are comfortable with, regardless of the speed limit.MetadataThe metadata for the contained layer of the NCDOT Speed Limit Service is available through the following link:Speed LimitPoint of Contact North Carolina Department of Information Technology -Transportation, GIS UnitGIS Data and Services ConsultantContact information:gishelp@ncdot.govCentury Center – Building B1020 Birch Ridge DriveRaleigh, NC 27610Hours of service: 9:00am - 5:00pm Monday – FridayContact instructions: Please send an email with any issues, questions, or comments regarding the Speed Limit data. If it is an immediate need, please indicate as such in the subject line in an email.NCDOT GIS Unit GO! NC Product TeamLastUpdated: 2024-01-01 00:00:00

Speed LimitsNote that there are a number of speed reversal projects currently underway and that these new speeds may not be reflected in this layer. For up-to-date information, please see the AT Speed limits being reversed in Auckland web site or the National Speed Limit Register from Waka Kotahi NZTA.Auckland Transport is also responsible for reviewing Speed Limits of the road network and if speeds are found to be unsafe, AT is obliged to set new safe and appropriate speeds. Data attributes include Road ID, Road Name, current speed limit and Length (Road length).Update Frequency: WeeklyContact: Auckland Transport Safe Speeds Programme Spatial Coverage: Auckland Region

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_9