Incorporating Driver Behavior Considerations in Safety Performance Estimates of Infrastructure Improvements

Driver characteristics are one of the most influential contributing factors to traffic crashes. However, the advancements in data-driven safety analysis tools available to safety practitioners are focused on infrastructure-related factors affecting crashes. Research is needed to include driver behavior factors in crash prediction models to allow for a more comprehensive assessment of existing and expected safety performance. Regression models for expected crash frequency and severity used for development of crash modification factors (CMFs) and estimation of predicted crashes do not incorporate driver characteristics. This creates a problem for those considering safety applications since one of the most important factors is not included. This will lead to safety solutions that may work as intended. In the Highway Safety Manual (HSM) the measures of driver characteristics are divided into several categories such as attention and information processing, vision, perception-reaction time, and speed choice. But these categories are provided at a very high level. The driver characteristics such as gender, age, speeding, blood alcohol content, seatbelt use, and distracted driving are usually reported by police officers called to the crash scenes. These factors can be used to assess the impact of driver characteristics on crash frequency, type, and severity. Several studies have evaluated the impact of these factors on crash severity. There is, however, a need to incorporate these factors in widely implemented crash prediction models such as Safety Performance Functions (SPF), Severity Distribution Functions (SDF) and other types of crash prediction models to achieve a better picture of the true potential impacts of safety decisions. Including the driver behavior factors will help improve the prediction accuracy of the aforementioned crash prediction models and will provide the highway safety agencies with a better assessment of the contributing factors of traffic crashes. The objective of this research is to develop a methodology to incorporate aggregate driver characteristics into safety models such as the SPFs and SDFs, as one or more explanatory variables in the models. The research will also identify the list of driver characteristics or factors that can be aggregated for a given segment, block group, and other spatial units, and used in safety model prediction. These findings will be incorporated into tools such as the HSM predicative methods.

Language

  • English

Project

  • Status: Proposed
  • Funding: $600000
  • Contract Numbers:

    Project 22-47

  • Sponsor Organizations:

    National Cooperative Highway Research Program

    Transportation Research Board
    500 Fifth Street, NW
    Washington, DC  United States  20001

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    American Association of State Highway and Transportation Officials (AASHTO)

    444 North Capitol Street, NW
    Washington, DC  United States  20001
  • Project Managers:

    Rogers, William

  • Start Date: 20191210
  • Expected Completion Date: 0
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01707550
  • Record Type: Research project
  • Source Agency: Transportation Research Board
  • Contract Numbers: Project 22-47
  • Files: TRB, RiP
  • Created Date: Jun 3 2019 3:17PM