A tree-structured crash surrogate measure for freeways

Yan Kuang, Xiaobo Qu, Shuaian Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

79 Citations (Scopus)

Abstract

In this paper, we propose a novel methodology to define and estimate a surrogate measure. By imposing a hypothetical disturbance to the leading vehicle, the following vehicle's action is represented as a probabilistic causal model. After that, a tree is built to describe the eight possible conflict types under the model. The surrogate measure, named Aggregated Crash Index (ACI), is thus proposed to measure the crash risk. This index reflects the accommodability of freeway traffic state to a traffic disturbance. We further apply this measure to evaluate the crash risks in a freeway section of Pacific Motorway, Australia. The results show that the proposed indicator outperforms the three traditional crash surrogate measures (i.e., Time to Collision, Proportion of Stopping Distance, and Crash Potential Index) in representing rear-end crash risks. The applications of this measure are also discussed.
Original languageEnglish
Pages (from-to)137-148
Number of pages12
JournalAccident Analysis and Prevention
Volume77
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Keywords

  • Crash surrogate measure
  • Hypothetical disturbance
  • Proactive safety evaluation

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health
  • Law

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