Reliability-Based Safety Risk and Cost Prediction of Large Trucks on Rural Highways

The primary causes of accidents involving large trucks on rural highways were found to be excessive speed and adverse driving conditions. Different from passenger vehicles, it is known that the safety performance of large trucks in adverse driving conditions greatly depends on the specific terrain and local weather conditions. By integrating both historical data analysis and simulations, a multi-scale investigation is conducted to evaluate the traffic safety of large trucks on mountainous interstate highways. Firstly, the ten-year historical accident records are analyzed to identify the accident-vulnerable-locations (AVLs) and site-specific critical adverse driving conditions. Secondly, a simulation-based single-vehicle assessment is performed for predicting the large-truck accident risks with the combination of given weather, topographical, road, and vehicle information at those AVLs along the entire corridor. A framework of a reliability-based assessment model of vehicle safety under adverse driving conditions is developed. Such a framework is built based on the advanced transient dynamic vehicle simulation models, which can consider the coupling effects between vehicles and adverse driving conditions, such as wind gust, snow-covered or icy road surfaces and/or curving. The single-vehicle safety index is introduced to provide rational assessment of accident risks by considering uncertainties of critical variables. Finally, geographic information system (GIS) maps with topographic conditions embedded are generated. By displaying the data on the GIS-based map, different accident risk indices can easily be displayed and compared on the GIS map. A typical mountainous highway in Colorado is studied for demonstration purposes.

  • Record URL:
  • Supplemental Notes:
    • This research was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    Colorado State University, Fort Collins

    Department of Civil and Environmental Engineering
    Fort Collins, CO  United States  80525

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108

    Research and Innovative Technology Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Chen, Suren
    • Chen, Feng
  • Publication Date: 2011-9


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 52p

Subject/Index Terms

Filing Info

  • Accession Number: 01360951
  • Record Type: Publication
  • Report/Paper Numbers: MPC Report No. 11-243
  • Files: UTC, TRIS, USDOT
  • Created Date: Jan 25 2012 2:21PM