An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions

    Liangguo Kang Affiliation
    ; Shuli Zhang Affiliation
    ; Chao Wu Affiliation


The lessons learned from each Traffic Collision (TC) will help safety practitioners to avoid similar occurrences in the future. However, few studies and methods have focused specifically on the similar features among different collisions. Thus, the development of a measurement method for investigating the best evidence on the causal factors of TCs was warranted. In this study, a similarity analysis method based on the Analytic Hierarchy Process (AHP) and Similarity (S) theory, the AHP-S method, was constructed. This method was designed to identify the similar elements and similar units of collision scenes according to the analysis criteria and sub-criteria and further to calculate the degree of similarity between recognized similar pairs among TCs. Six TC cases were randomly selected as examples, and the degrees of similarity between cases 1 to 5 and case 6 were calculated separately. The calculation results showed that out of the five collision cases (cases 1–5), case 1 provided the best evidence for analysing the causal factors of case 6. This study promotes the development of quantitative analysis methods for collision incidents and provides an effective evidence-based method for TC avoidance.

First published online 17 March 2021

Keyword : traffic collision, causal factors, similarity analysis, similar evidence, collision analysis

How to Cite
Kang, L., Zhang, S., & Wu, C. (2021). An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions. Transport, 36(5), 376-385.
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Dec 16, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.


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