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

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 investigati...

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Autores principales:	Liangguo Kang, Shuli Zhang, Chao Wu
Formato:	article
Lenguaje:	EN
Publicado:	Vilnius Gediminas Technical University 2021
Materias:	traffic collision causal factors similarity analysis similar evidence collision analysis Transportation engineering TA1001-1280
Acceso en línea:	https://doaj.org/article/776f462793fc4b2298552b948630563c
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spelling	oai:doaj.org-article:776f462793fc4b2298552b948630563c2021-11-25T13:02:47ZAn approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions1648-41421648-348010.3846/transport.2021.14329https://doaj.org/article/776f462793fc4b2298552b948630563c2021-03-01T00:00:00Zhttps://journals.vgtu.lt/index.php/Transport/article/view/14329https://doaj.org/toc/1648-4142https://doaj.org/toc/1648-3480The 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 2021Liangguo KangShuli ZhangChao WuVilnius Gediminas Technical Universityarticletraffic collisioncausal factorssimilarity analysissimilar evidencecollision analysisTransportation engineeringTA1001-1280ENTransport, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	traffic collision causal factors similarity analysis similar evidence collision analysis Transportation engineering TA1001-1280
spellingShingle	traffic collision causal factors similarity analysis similar evidence collision analysis Transportation engineering TA1001-1280 Liangguo Kang Shuli Zhang Chao Wu An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
description	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
format	article
author	Liangguo Kang Shuli Zhang Chao Wu
author_facet	Liangguo Kang Shuli Zhang Chao Wu
author_sort	Liangguo Kang
title	An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
title_short	An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
title_full	An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
title_fullStr	An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
title_full_unstemmed	An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
title_sort	approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
publisher	Vilnius Gediminas Technical University
publishDate	2021
url	https://doaj.org/article/776f462793fc4b2298552b948630563c
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An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions

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