Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data
The emerging connected vehicle and Automated Driving System (ADS), the widely available advanced in-vehicle telematics data collection/transmitting systems, as well as smartphone apps produce gigantic amount of high-frequency, high-resolution driving data. These telematics data provide comprehensive information on driving style, driving environment, road condition, and vehicle conditions. The high frequency telematics data has been used for several safety areas such as insurance pricing, teenage driving risk evaluation, and fleet safety management. This report study advances traffic safety analysis in the follow aspects: (1) characterize the high-frequency kinematic signatures for safety critical events compared to normal operations; and (2) develop models to distinguish and predict crashes from normal driving scenarios based on the high frequency data. Two deep learning models were developed. The first one combines the strength of convolutional neural network (CNN), gated recurrent unit (GRU) network and extreme gradient boosting (XGBoost). The second approach is based on a novel variational inference for extremes (VIE) to address the rarity of crashes. The models proposed in this project can benefit a variety of traffic research and applications including connected vehicles and ADS real-time safety monitoring, naturalistic driving study (NDS) data analysis, ride-hailing safety prediction, as well as fleet and driver safety management programs.
- Record URL:
- Summary URL:
- Record URL:
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Supplemental Notes:
- This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
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Corporate Authors:
Safety through Disruption University Transportation Center (Safe-D)
Virginia Tech Transportation Institute
Blacksburg, VA United States 24060Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Authors:
- Guo, Feng
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0000-0002-2572-481X
- Qian, Chen
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0000-0002-0968-4969
- Shi, Liang
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0000-0002-9308-333X
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Digital/other
- Edition: Final Report
- Features: Figures; References; Tables;
- Pagination: 27p
Subject/Index Terms
- TRT Terms: Automatic data collection systems; Crash risk forecasting; Driving; Machine learning; Risk assessment; Telematics; Traffic safety
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01845848
- Record Type: Publication
- Report/Paper Numbers: VTTI-00-028
- Contract Numbers: 69A3551747115
- Files: UTC, NTL, TRIS, ATRI, USDOT
- Created Date: May 20 2022 9:30AM