Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data

The emerging connected vehicle and Automated Driving System (ADS) as well as widely available advanced in-vehicle telematics data collection/transmitting systems 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 condition. The telematics data has been used for a number of safety areas such as insurance pricing, teenage driving risk evaluation, and fleet safety management. The surge of ride-hailing service in the last decade provides a novel alternative mode for travelers. The ride-hailing drivers are a unique driver population with substantial operational responsibilities and the safety management is critical for the drivers. The smartphone ride-hailing app can conveniently collect kinematic information from sensors on smartphones, thus make the telematics data available for the entire driver population. Parallel to this proposed study, the research team has evaluated telematics feature in prediction crash risk for millions of ride-hailing drivers. This project will address the following main safety research questions using high-frequency, high resolution telematics data: 1) characterize the high-frequency kinematic signatures for safety critical events as well as during normal operations; 2) develop models to predict high risk drivers based on the kinematics signatures. 3) develop models to distinguish and predict crashes from normal driving scenarios based on the high frequency data. The project will contribute to connected vehicles and ADS real-time safety monitoring, NDS data analysis, hail-driving driver safety prediction, as well as fleet and driver safety management programs.


  • English


  • Status: Active
  • Funding: $150,000
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety through Disruption University Transportation Center (Safe-D)

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Glenn, Eric

  • Performing Organizations:

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061
  • Principal Investigators:

    Guo, Feng

  • Start Date: 20191215
  • Expected Completion Date: 20210830
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01737838
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Apr 26 2020 12:07PM