Identifying Risk Factors for Urban Expressway Traffic Crashes Using Multilevel Bayesian Models with Unprocessed Automatic Vehicle Identification Data

Crash frequency analysis of total crashes is the foremost step in aggregate traffic safety studies. A multi-level Bayesian framework has been developed to identify the risk factors contributing to crashes on State Road 408, a segment of an urban expressway. The incorporation of unprocessed Automatic Vehicle Identification (AVI) data in traffic safety study was evaluated and justified. The effects of traffic data and roadway geometric data were modeled as a two-level structure. The Random-parameters approach is utilized at both levels for its flexibility in accounting for heterogeneity at different levels. A mixture of fixed and random parameters for the geometric variables outperforms models with only fixed or random parameters. It is proven that heavier traffic at lower speed and higher speed variation could significantly increase the crash likelihood. As for the implication on practice, Dynamic Message Signs could be used to display warning signs about the traffic conditions. Geometric design of segments with auxiliary lanes, markings and signs at these segments need close examination to provide drivers with clear guidance and some leeway for driving errors.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANB20 Safety Data, Analysis and Evaluation.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Shi, Qi
  • Abdel-Aty, Mohamed A
  • Yu, Rongjie
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 15p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Automatic vehicle identification; Automobile drivers; Bayes' theorem; Crash rates; Expressways; Frequency distributions; Highway safety; Risk assessment; Road markings; Traffic congestion; Traffic crashes; Traffic flow; Urban areas; Variable message signs
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; I81: Accident Statistics; I82: Accidents and Transport Infrastructure; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01520316
• Record Type: Publication
• Report/Paper Numbers: 14-0303
• Files: TRIS, TRB, ATRI
• Created Date: Mar 27 2014 3:38PM