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Analysis of maritime transport accidents using Bayesian networks

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  • Shiqi Fan
  • Zaili Yang
  • Eduardo Blanco-Davis
  • Jinfen Zhang
  • Xinping Yan

Abstract

A Bayesian network–based risk analysis approach is proposed to analyse the risk factors influencing maritime transport accidents. Comparing with previous studies in the relevant literature, it reveals new features including (1) new primary data directly derived from maritime accident records by two major databanks Marine Accident Investigation Branch and Transportation Safety Board of Canada from 2012 to 2017, (2) rational classification of the factors with respect to each of the major types of maritime accidents for effective prevention, and (3) quantification of the extent to which different combinations of the factors influence each accident type. The network modelling the interdependency among the risk factors is constructed by using a naïve Bayesian network and validated by sensitivity analysis. The results reveal that the common risk factors among different types of accidents are ship operation, voyage segment, ship type, gross tonnage, hull type, and information. Scenario analysis is conducted to predict the occurrence likelihood of different types of accidents under various situations. The findings provide transport authorities and ship owners with useful insights for maritime accident prevention.

Suggested Citation

  • Shiqi Fan & Zaili Yang & Eduardo Blanco-Davis & Jinfen Zhang & Xinping Yan, 2020. "Analysis of maritime transport accidents using Bayesian networks," Journal of Risk and Reliability, , vol. 234(3), pages 439-454, June.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:3:p:439-454
    DOI: 10.1177/1748006X19900850
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    References listed on IDEAS

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    Cited by:

    1. Jun Shen & Xiaoxue Ma & Weiliang Qiao, 2022. "A Model to Evaluate the Effectiveness of the Maritime Shipping Risk Mitigation System by Entropy-Based Capability Degradation Analysis," IJERPH, MDPI, vol. 19(15), pages 1-34, July.
    2. Zhang, Jinfeng & Jin, Mei & Wan, Chengpeng & Dong, Zhijie & Wu, Xiaohong, 2024. "A Bayesian network-based model for risk modeling and scenario deduction of collision accidents of inland intelligent ships," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. 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.
    4. Li, Huanhuan & Çelik, Cihad & Bashir, Musa & Zou, Lu & Yang, Zaili, 2024. "Incorporation of a global perspective into data-driven analysis of maritime collision accident risk," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    5. Fan, Shiqi & Blanco-Davis, Eduardo & Yang, Zaili & Zhang, Jinfen & Yan, Xinping, 2020. "Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Ung, S.T., 2021. "Navigation Risk estimation using a modified Bayesian Network modeling-a case study in Taiwan," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    7. Xiaoyuan Zhao & Haiwen Yuan & Qing Yu, 2021. "Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    8. 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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