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Risk analysis of maritime accidents along the main route of the Maritime Silk Road: a Bayesian network approach

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  • Meizhi Jiang
  • Jing Lu
  • Zaili Yang
  • Jing Li

Abstract

The safety of maritime transportation along the twenty-first century Maritime Silk Road (MSR) is important to ensure its development and sustainability. Maritime transportation poses risks of accidents that can cause the death or injury of crew members and damage to ships and the environment. This paper proposes a Bayesian network (BN) based risk analysis approach that is newly applied in the main route of the MSR to analyse its relevant maritime accidents. The risk data are manually collected from the reports of the accident that occurred along the MSR. Next, the risk factors are identified and the results from the modelling method can provide useful insights for accident prevention. Historical data collected from accident reports are used to estimate the prior probabilities of the identified risk factors influencing the occurrence of maritime accidents. The results show that the main influencing factors are the type and location of an accident and the type, speed, and age of the involved ship(s). In addition, scenario analysis is conducted to analyse the risks of different ships in various navigational environments. The findings can be used to analyse the probability of each possible maritime accident along MSR and to provide useful insights for shipowners’ accident prevention.

Suggested Citation

  • Meizhi Jiang & Jing Lu & Zaili Yang & Jing Li, 2020. "Risk analysis of maritime accidents along the main route of the Maritime Silk Road: a Bayesian network approach," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(6), pages 815-832, August.
    • Handle: RePEc:taf:marpmg:v:47:y:2020:i:6:p:815-832
    DOI: 10.1080/03088839.2020.1730010
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    Cited by:

    1. Guo, Yunlong & Jin, Yongxing & Hu, Shenping & Yang, Zaili & Xi, Yongtao & Han, Bing, 2023. "Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Munim, Ziaul Haque & Sørli, Michael André & Kim, Hyungju & Alon, Ilan, 2024. "Predicting maritime accident risk using Automated Machine Learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Hongxia Zhou & Fang Wang & Weili Hu & Manel Grifoll & Jiao Liu & Weijie Du & Pengjun Zheng, 2024. "A Novel Framework for Identifying Major Fishing Vessel Accidents and Their Key Influencing Factors," Sustainability, MDPI, vol. 16(18), pages 1-19, September.
    4. 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).
    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).
    6. Çakır, Erkan & Fışkın, Remzi & Sevgili, Coşkan, 2021. "Investigation of tugboat accidents severity: An application of association rule mining algorithms," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    7. Liangxia Zhong & Jiaxin Wu & Yiqing Wen & Bingjie Yang & Manel Grifoll & Yunping Hu & Pengjun Zheng, 2023. "Analysis of Factors Affecting the Effectiveness of Oil Spill Clean-Up: A Bayesian Network Approach," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    8. Zhou, Yusheng & Li, Xue & Yuen, Kum Fai, 2022. "Holistic risk assessment of container shipping service based on Bayesian Network Modelling," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Jiang, Meizhi & Lu, Jing, 2020. "The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).

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