Risk-Based Advisory Prevention System for Commercial Trucks Under Hazardous Conditions

In adverse driving conditions, such as inclement weather and complex terrain, large trucks are often involved in single-vehicle (SV) accidents in addition to multi-vehicle (MV) accidents. Although the absolute number of SV accidents is often lower than that of MV accidents, SV accidents usually result in more serious injury and fatality. Ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) are studied to investigate the difference in driver-injury severity between SV and MV accidents using multinomial logit models. Injury severity from SV and MV accidents involving trucks on rural highway is modeled separately and their respective critical risk factors, such as driver, vehicle, temporal, roadway, environmental, and accident characteristics, are evaluated. The study shows that there is substantial difference between the impacts from a variety of variables on the driver-injury severity in MV and SV accidents. By conducting the injury severity study for MV and SV accidents involving trucks separately, some new or more comprehensive observations, which have not been covered in the existing studies, can be made. As a result, the complex interactions of variables and the nature of truck-driver injury are able to be disclosed in a better way. Based on the improved understanding on the severity of truck drivers' injuries from truck-involved accidents, it is expected that more rational and effective injury prevention strategy may be developed for truck drivers under different driving conditions in the future. Based on the model developed for injury studies of truck drivers, risk-based prevention strategy can be realistically developed.

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

    Colorado State University, Fort Collins

    Fort Collins, CO  United States  80523

    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: References; Tables;
  • Pagination: 33p

Subject/Index Terms

Filing Info

  • Accession Number: 01361115
  • Record Type: Publication
  • Report/Paper Numbers: MPC Report No. 11-242
  • Created Date: Jan 25 2012 2:22PM