An approach for traffic collision avoidance: measuring the similar evidence on the causal factors of collisions
Abstract
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. https://doi.org/10.3846/transport.2021.14329
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chen, F.; Wu, J.; Chen, X.; Wang, J.; Wang, D. 2016. Benchmarking road safety performance: Identifying a meaningful reference (best-in-class), Accident Analysis & Prevention 86: 76–89. https://doi.org/10.1016/j.aap.2015.10.018
Chen, M.; Yuan, X.; Pan, M.; Xie, Z. 2004. Discussion on preventive measures for road traffic accidents from statistic analysis and comparison, China Safety Science Journal (8): 59–63. (in Chinese).
Cvitanić, D.; Vukoje, B. 2018. Detection and analysis of hazardous locations on roads: a case study of the Croatian motorway A1, Transport 33(2): 418–428. https://doi.org/10.3846/16484142.2016.1259180
Duleba, S. 2019. An AHP-ISM approach for considering public preferences in a public transport development decision, Transport 34(6): 662–671. https://doi.org/10.3846/transport.2019.9080
Fabjanowicz, M.; Bystrzanowska, M.; Namieśnik, J.; Tobiszewski, M.; Płotka-Wasylka, J. 2018. An analytical hierarchy process for selection of the optimal procedure for resveratrol determination in wine samples, Microchemical Journal 142: 126–134. https://doi.org/10.1016/j.microc.2018.06.028
Farooq, D.; Moslem, S.; Duleba, S. 2019. Evaluation of driver behavior criteria for evolution of sustainable traffic safety, Sustainability 11(11): 3142. https://doi.org/10.3390/su11113142
Fernandes, R.; Hatfield, J.; Soames Job, R. F. 2010. A systematic investigation of the differential predictors for speeding, drink-driving, driving while fatigued, and not wearing a seat belt, among young drivers, Transportation Research Part F: Traffic Psychology and Behaviour 13(3): 179–196. https://doi.org/10.1016/j.trf.2010.04.007
Gao, Y.; Dong, X.; Tian, F. 2015. Present situation analysis on highway traffic safety and management countermeasures, Journal of Safety Science and Technology (10): 110–115. (in Chinese).
Goel, G.; Sachdeva, S. N. 2016. Analysis of road accidents on NH-1 between RD 98 km to 148 km, Perspectives in Science 8: 392–394. https://doi.org/10.1016/j.pisc.2016.04.086
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Ho, W.; Ma, X. 2018. The state-of-the-art integrations and applications of the analytic hierarchy process, European Journal of Operational Research 267(2): 399–414. https://doi.org/10.1016/j.ejor.2017.09.007
Hu, L.; Li, Y. 2014. Impact analysis on typical transport facilities to causes of road traffic accidents, Journal of Wuhan University of Technology (Transportation Science & Engineering) (1): 98–102. (in Chinese).
Hruška, R.; Průša, P.; Babić, D. 2014. The use of AHP method for selection of supplier, Transport 29(2): 195–203. https://doi.org/10.3846/16484142.2014.930928
Ilbahar, E.; Karasan, A.; Cebi, S.; Kahraman, C. 2018. A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system, Safety Science 103: 124–136. https://doi.org/10.1016/j.ssci.2017.10.025
Jia, N.; Wu, C.; Huang, L.; Wang, B. 2016. Methodology of similarity safety systematics research, China Safety Science Journal (6): 30–35. (in Chinese).
Li, S.-C.; Xiao, L. 2016a. Statistics of industrial accidents in China during the period from January to February in 2016, Journal of Safety and Environment (2): 395–396. (in Chinese).
Li, S.-C.; Xiao, L. 2016b. Statistics of industrial accidents in China during the period from March to April in 2016, Journal of Safety and Environment (3): 395–396. (in Chinese).
Li, S.-C.; Xiao, L. 2016c. Statistics of industrial accidents in China during the period from May to June in 2016, Journal of Safety and Environment (4): 395–396. (in Chinese).
Li, S.-C.; Xiao, L. 2016d. Statistics of industrial accidents in China during the period from July to August in 2016, Journal of Safety and Environment (5): 395–396. (in Chinese).
Li, S.-C.; Xiao, L. 2016e. Statistics of industrial accidents in China during the period from September to October in 2016, Journal of Safety and Environment 16(6): 395–396. (in Chinese).
Li, S.-C.; Xiao, L. 2017. Statistics of industrial accidents in China during the period from November to December in 2016, Journal of Safety and Environment 17(1): 395–396. (in Chinese).
Llopis-Castelló, D.; Camacho-Torregrosa, F. J.; García, A. 2018. Development of a global inertial consistency model to assess road safety on Spanish two-lane rural roads, Accident Analysis & Prevention 119: 138–148. https://doi.org/10.1016/j.aap.2018.07.018
Lum, H.; Reagan, J. A. 1995. Interactive highway safety design model: accident predictive module, Public Roads 58(3): 14–17.
MacLean, A. W.; Davies, D. R. T.; Thiele, K. 2003. The hazards and prevention of driving while sleepy, Sleep Medicine Reviews 7(6): 507–521. https://doi.org/10.1016/S1087-0792(03)90004-9
Matírnez, A.; Mántaras, D. A.; Luque, P. 2013. Reducing posted speed and perceptual countermeasures to improve safety in road stretches with a high concentration of accidents, Safety Science 60: 160–168. https://doi.org/10.1016/j.ssci.2013.07.003
Navestad, T.-O.; Phillips, R. O.; Elvebakk, B. 2015. Traffic accidents triggered by drivers at work – a survey and analysis of contributing factors, Transportation Research Part F: Traffic Psychology and Behaviour 34: 94–107. https://doi.org/10.1016/j.trf.2015.07.024
Pérez, J. A.; Gonçalves, G. R.; Rangel, J. M. G.; Ortega, P. F. 2019. Accuracy and effectiveness of orthophotos obtained from low cost UASs video imagery for traffic accident scenes documentation, Advances in Engineering Software 132: 47–54. https://doi.org/10.1016/j.advengsoft.2019.03.010
Podvezko, V.; Sivilevičius, H. 2013. The use of AHP and rank correlation methods for determining the significance of the interaction between the elements of a transport system having a strong influence on traffic safety, Transport 28(4): 389–403. https://doi.org/10.3846/16484142.2013.866980
Prentkovskis, O.; Sokolovskij, E.; Bartulis, V. 2010. Investigating traffic accidents: a collision of two motor vehicles, Transport 25(2): 105–115. https://doi.org/10.3846/transport.2010.14
Saaty, T. L. 2008. Decision making with the analytic hierarchy process, International Journal of Services Sciences 1(1): 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Sarkar, C.; Webster, C.; Kumari, S. 2018. Street morphology and severity of road casualties: a 5-year study of Greater London, International Journal of Sustainable Transportation 12(7): 510–525. https://doi.org/10.1080/15568318.2017.1402972
SAWS. 2016. Accident Investigation Reports. State Administration of Work Safety (SAWS), China. Available from Internet: http://english.www.gov.cn/state_council/2014/09/09/content_281474986284037.htm (in Chinese).
SC PRC. 2007. Report on Work Accident and Regulations of Investigation and Treatment. The State Council, The People’s Republic of China (SC PRC). Available from Internet: http://english.www.gov.cn (in Chinese).
Stević, Ž.; Vasiljević, M.; Puška, A.; Tanackov, I.; Junevičius, R.; Vesković, S. 2019. Evaluation of suppliers under uncertainty: a multiphase approach based on fuzzy AHP and fuzzy EDAS, Transport 34(1): 52–66. https://doi.org/10.3846/transport.2019.7275
Sze, N. N.; Song, Z. 2019. Factors contributing to injury severity in work zone related crashes in New Zealand, International Journal of Sustainable Transportation 13(2): 148–154. https://doi.org/10.1080/15568318.2018.1452083
Vorko-Jović, A.; Kern, J.; Biloglav, Z. 2006. Risk factors in urban road traffic accidents, Journal of Safety Research 37(1): 93–98. https://doi.org/10.1016/j.jsr.2005.08.009
Wang, B.; Wu, C.; Shi, B.; Huang, L. 2017. Evidence-based safety (EBS) management: a new approach to teaching the practice of safety management (SM), Journal of Safety Research 63: 21–28. https://doi.org/10.1016/j.jsr.2017.08.012
Wu, C.; Jia, N. 2016. Establishment of similarity safety systematics, Systems Engineering-Theory & Practice (5): 1354–1360. (in Chinese).
Wu, J.; Subramanian, R.; Craig, M.; Starnes, M.; Longthorne, A. 2013. The effect of earlier or automatic collision notification on traffic mortality by survival analysis, Traffic Injury Prevention 14: S50–S57. https://doi.org/10.1080/15389588.2013.799279
Zhao, J.; Deng, W. 2015. The use of Bayesian network in analysis of urban intersection crashes in China, Transport 30(4): 411–420. https://doi.org/10.3846/16484142.2013.816365
Zhou, P.; Ang, B. W. 2009. Comparing MCDA aggregation methods in constructing composite indicators using the Shannon–Spearman measure, Social Indicators Research 94(1): 83–96. https://doi.org/10.1007/s11205-008-9338-0