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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Vilnius Gediminas Technical University
2021-03-01
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| Series: | Transport |
| Subjects: | |
| Online Access: | https://journals.vgtu.lt/index.php/Transport/article/view/14329 |
| _version_ | 1828970678189031424 |
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| author | Liangguo Kang Shuli Zhang Chao Wu |
| author_facet | Liangguo Kang Shuli Zhang Chao Wu |
| author_sort | Liangguo Kang |
| collection | DOAJ |
| 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 |
| first_indexed | 2024-12-14T12:56:18Z |
| format | Article |
| id | doaj.art-776f462793fc4b2298552b948630563c |
| institution | Directory Open Access Journal |
| issn | 1648-4142 1648-3480 |
| language | English |
| last_indexed | 2024-12-14T12:56:18Z |
| publishDate | 2021-03-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Transport |
| spelling | doaj.art-776f462793fc4b2298552b948630563c2022-12-21T23:00:34ZengVilnius Gediminas Technical UniversityTransport1648-41421648-34802021-03-0111010.3846/transport.2021.1432914329An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisionsLiangguo Kang0Shuli Zhang1Chao Wu2School of Resources and Safety Engineering, Central South University, Changsha, China; Safety and Security Theory Innovation and Promotion Center, Central South University, Changsha, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, China; Taiyuan Municipal Public Utilities Administration, Taiyuan, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, China; Safety and Security Theory Innovation and Promotion Center, Central South University, Changsha, ChinaThe 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 2021https://journals.vgtu.lt/index.php/Transport/article/view/14329traffic collisioncausal factorssimilarity analysissimilar evidencecollision analysis |
| spellingShingle | Liangguo Kang Shuli Zhang Chao Wu An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions Transport traffic collision causal factors similarity analysis similar evidence collision analysis |
| title | 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_short | 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 |
| topic | traffic collision causal factors similarity analysis similar evidence collision analysis |
| url | https://journals.vgtu.lt/index.php/Transport/article/view/14329 |
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