Connected Vehicle Data Safety Applications

Phase I: Today’s connected vehicles have an abundance of electronics and sensors that can passively collect data on driving behaviors, mechanical status, and physical roadway conditions. These data can potentially help safety professionals better understand relationships between driving events and risk. This project will explore roadway safety applications of connected vehicle data provided by Wejo for July and October 2019 for the entire state of Texas. Wejo is a connected vehicle data vendor that aggregates data from automotive manufacturers and then licenses its use to customers. The Wejo data consist of vehicle movement data with 3-second waypoint frequency and driver event data for individual vehicle trips. From these, this project will look at actual travel speeds, seat belt usage, harsh braking/acceleration by date/time, location, and vehicle year/make/model. Crash data will be spatially related to driving events to explore possible statistic relationships between driving events/behavior and crash risk. Phase II: A cloud computing system of services and spatial algorithms were designed and developed to process the very large amounts of connected car (CC) data from Wejo to develop model variables, visualizations, and descriptive statistics. This research seeks to leverage those methods to explore the unanswered question of whether commercially available CC data derived from automotive OEMs can be used for roadway safety applications. The idea is that if leading crash risk indicators can be developed from CC data, then areas of safety concern can be detected before crashes occur, thereby saving lives, time, and resources. Some major automotive OEMs have developed tools to access and visualize the data generated by their CCs but are still lacking the ability to provide risk-based conclusions. This project will evaluate the effectiveness of commercially available CC data in roadway safety applications. We will comprehensively explore the relationships between driving behaviors and different severity crash events. An innovative big data analytic framework will be developed to analyze this emerging safety data.


  • English


  • Status: Active
  • Funding: $75000
  • 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:

    Texas A&M Transportation Institute (TTI)

    400 Harvey Mitchell Parkway South
    Suite 300
    College Station, TX  United States  77845-4375
  • Principal Investigators:

    Martin, Michael

  • Start Date: 20200201
  • Expected Completion Date: 20230901
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01737733
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Apr 23 2020 10:51PM