Data from: Understanding real-world brake activity: A key to assessing non-tailpipe emission sources for sustainable transportation and communities
Data files
May 19, 2025 version files 3.19 GB
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155xy_magnitude_data_10hz.csv
5.47 KB
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163xy_magnitude_data_10hz.csv
4.16 KB
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403xy_magnitude_data_10hz.csv
3.35 KB
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575xy_magnitude_data_10hz.csv
2.97 KB
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718xy_magnitude_data_10hz.csv
2.55 KB
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COM13_250103_014834.ubx
2.88 MB
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egovehicle_trip_0.csv
9.93 KB
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egovehicle_trip_1.csv
9.82 KB
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egovehicle_trip_2.csv
9.67 KB
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NCST250102_0.7z
3.06 GB
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output_2025-01-02_18-01-03.txt
2.63 MB
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preds.7z
119.06 MB
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README.md
4.24 KB
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tableview_250127_050942.csv
602.24 KB
Abstract
Electrification is considered a promising solution to environmental sustainability, due to the removal of tailpipe emissions (during operation) from the transportation sector. However, this would not have too much effect on those non-tailpipe particulate emissions. In addition, brake and tire wear particles are composed of various metals, rubber compounds, and organics which includes adhesives and have potential higher risks on the community health effects. In this study, the research team proposes to: 1) measure real-world brake activities of a large volume of vehicles traversing major roadway segments (e.g., near signalized intersections) by leveraging advanced roadside sensing technologies, e.g., Light Detection And Ranging and/or high-definition camera, as well as deep learning-based computer vision algorithms; and 2) construct the real-world brake activity database and supplement for the non-tailpipe emissions inventory. It is expected that this study will contribute to the improvement of non-tailpipe emissions modeling and understanding. The findings from this study will help address the environmental justice issues in disadvantaged communities.
https://doi.org/10.5061/dryad.41ns1rnrp
Description of the data and file structure
This dataset compiles multi-sensor data collected to analyze vehicle braking events and associated non-tailpipe particulate matter emissions at urban intersections. Data sources include GPS logs, LiDAR point clouds, video recordings, and onboard vehicle sensors. The dataset supports research on vehicle dynamics, braking behavior analysis, trajectory refinement, and emission estimation.
Files and variables
File: tableview_250127_050942.csv
Description:
Variables
- Index: Sequential index of the data points.
- Lat (degrees): Latitude of the vehicle's location.
- Lon (degrees): Longitude of the vehicle's location.
- CoG (degrees): Course over ground (direction of movement).
- SoG (m/s): Speed over ground (vehicle speed).
- UTC ((hh:mm:ss)): Coordinated Universal Time timestamp.
- Alt (MSL) (m): Altitude above mean sea level.
- Alt (HAE) (m): Altitude above the WGS84 ellipsoid.
File: COM13_250103_014834.ubx
Description: This is raw GPS data of the probe vehicle collected by a u-blox GPS receiver. Similar to the CSV file above, it includes fields like Index, Lat, Lon, CoG, SoG, UTC, Alt (MSL), and Alt (HAE).
File: NCST250102_0.7z
Description: A compressed (7z) archive of the raw LiDAR point cloud data (DB3 format), providing a compact alternative to the standalone DB3 file.
File: output_2025-01-02_18-01-03.txt
Description: This file contains brake status and brake pressure data of the probe vehicle.
Variables
- time: Universal Time timestamp.
- BRAKE_PRESSED: Status of the brake on/off
- BRAKE_PRESSURE (PSI): Brake pressure in PSI
File: egovehicle_trip_2.csv
Description: This is processed probe vehicle data in CSV format.
Variables
- Time (s): Time elapsed in seconds.
- Acceleration (m/s^2): Vehicle acceleration in meters per second squared.
- Speed (m/s): Vehicle speed in meters per second.
- Distance (m): Distance traveled in meters.
- Brake Pressure (PSI): Brake pressure in pounds per square inch.
- Brake Status: Status of the brake system.
File: egovehicle_trip_1.csv
Description: Same as the previous file (egovehicle_trip_2.csv), containing processed probe vehicle data.
File: egovehicle_trip_0.csv
Description: Same as the previous files (egovehicle_trip_2.csv and egovehicle_trip_1.csv), containing processed probe vehicle data.
File: preds.7z
Description: This is a compressed archive containing LiDAR detection bounding box results.
Content:
- The archive contains multiple files named by timestamps.
- Each file includes bounding box information
- Bounding box center position (x, y, z in meter)
- Bounding box size (length in meter)
- Bounding box orientation (pitch, roll, yaw in degree)
- classification (standardized vehicle categories)
- confidence scores (0-1)
File: 155xy_magnitude_data_10hz.csv
Description: LiDAR-detected, refined trajectory and brake status data (labeled via camera observations).
Variables
- Time (s): Time elapsed in seconds.
- Acceleration (m/s^2): Vehicle acceleration in meters per second squared.
- Speed (m/s): Vehicle speed in meters per second.
- Distance (m): Distance to intersection in meters.
- Brake Status: Status of the brake system.
File: 163xy_magnitude_data_10hz.csv
Description: A similar refined trajectory file with brake status labels, in the same format as the 155xy file.
File: 403xy_magnitude_data_10hz.csv
Description: A similar refined trajectory file with brake status labels, in the same format as the 155xy file.
File: 575xy_magnitude_data_10hz.csv
Description: A similar refined trajectory file with brake status labels, in the same format as the 155xy file.
File: 718xy_magnitude_data_10hz.csv
Description: A similar refined trajectory file with brake status labels, in the same format as the 155xy file.