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Risk analysis of bicycle accidents: A Bayesian approach

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  • Yang, Zaili
  • Yang, Zhisen
  • Smith, John
  • Robert, Bostock Adam Peter

Abstract

Cycling helps reduce traffic congestion, environmental pollution and promote a healthy lifestyle for the general public. However, it could also expose cyclists to dangerous environments, resulting in severe consequences and even death. Transport authorities are seeing growing accidents in city regions with increasing cycling population, requiring the development of new risk informed cycling safety policies. This paper aims to develop a new conceptual risk analysis approach based on a Bayesian network (BN) technique to enable the analysis and prediction of the severity of cycling accidents. To identify the risk factors influencing accident severity, 2,269 cycling accident reports from the UK city region were manually collected, where primary data was extracted and analysed. An advanced data training method (i.e. Tree Augmented Naïve Bayes (TAN)) for BN development was applied to investigate their correlation and their individual and combined contributions to cycling accident severity. As a result, the risk factors influencing accident severity are prioritised in terms of their risk contribution. The risk levels of accident severity can be predicted in dynamic situations based on the data from simulated and/or real cycling environments. The findings can provide useful insights for making rational cycling safety policies in proportion to different risk levels.

Suggested Citation

  • Yang, Zaili & Yang, Zhisen & Smith, John & Robert, Bostock Adam Peter, 2021. "Risk analysis of bicycle accidents: A Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000284
    DOI: 10.1016/j.ress.2021.107460
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    References listed on IDEAS

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    1. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    2. VANDENBULCKE, Grégory & THOMAS, Isabelle & INT PANIS, Luc, 2014. "Predicting cycling accident risk in Brussels: a spatial case-control approach," LIDAM Reprints CORE 2535, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Hänninen, Maria & Kujala, Pentti, 2012. "Influences of variables on ship collision probability in a Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 27-40.
    4. Molina Serrano, Beatriz & González-Cancelas, Nicoleta & Soler-Flores, Francisco & Camarero-Orive, Alberto, 2018. "Classification and prediction of port variables using Bayesian Networks," Transport Policy, Elsevier, vol. 67(C), pages 57-66.
    5. Pucher, J. & Dijkstra, L., 2003. "Promoting Safe Walking and Cycling to Improve Public Health: Lessons from The Netherlands and Germany," American Journal of Public Health, American Public Health Association, vol. 93(9), pages 1509-1516.
    6. Yang, Min & Wu, Jingxian & Rasouli, Soora & Cirillo, Cinzia & Li, Dawei, 2017. "Exploring the impact of residential relocation on modal shift in commute trips: Evidence from a quasi-longitudinal analysis," Transport Policy, Elsevier, vol. 59(C), pages 142-152.
    7. Yang, Zhisen & Yang, Zaili & Yin, Jingbo & Qu, Zhuohua, 2018. "A risk-based game model for rational inspections in port state control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 477-495.
    8. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    9. Zhang, D. & Yan, X.P. & Yang, Z.L. & Wall, A. & Wang, J., 2013. "Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 93-105.
    10. Krause, Jette & Small, Mitchell J. & Haas, Armin & Jaeger, Carlo C., 2016. "An expert-based bayesian assessment of 2030 German new vehicle CO2 emissions and related costs," Transport Policy, Elsevier, vol. 52(C), pages 197-208.
    11. Robert Thorndike, 1953. "Who belongs in the family?," Psychometrika, Springer;The Psychometric Society, vol. 18(4), pages 267-276, December.
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    Cited by:

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    2. Fang Wang & Weijie Du & Hongxiang Feng & Yun Ye & Manel Grifoll & Guiyun Liu & Pengjun Zheng, 2023. "Identification of Risk Influential Factors for Fishing Vessel Accidents Using Claims Data from Fishery Mutual Insurance Association," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    3. Zhang, Hengqi & Geng, Hua, 2023. "A methodology to identify and assess high-risk causes for electrical personal accidents based on directed weighted CN," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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    5. Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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