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Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection

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  • Wang, Yuhong
  • Zhang, Fan
  • Yang, Zhisen
  • Yang, Zaili

Abstract

Port State Control (PSC) inspection aids to control substandard ships and ensure safety at sea. Current risk-based PSC research and practice fail to incorporate ship deficiency records into detention probability analysis, because of the difficulty introduced by the involved big deficiency data. In this paper, a new Bayesian Network (BN) based PSC risk probabilistic model is developed to analyze the dependency and interdependency among the risk factors influencing PSC inspections based on big data derived from the inspection database of Tokyo MoU for the period between 2014 and 2017. The results reveal that ship's safety condition related deficiencies as well as technical features of the inspected vessel itself are among the most influential factors concerning PSC inspections and ship detention. New Bayesian learning methods are used to improve the model efficiency in ship detention prediction. As a result, the newly developed model has shown a reliable performance on dynamic prediction and cause-effect diagnosis of ship detention probabilities by pioneering the incorporation of ship deficiency records in the analysis. The findings provide important insights on how to facilitate risk-based PSC inspections for both ship owners and port states. They provide support for port state authorities to implement rational inspection policies.

Suggested Citation

  • Wang, Yuhong & Zhang, Fan & Yang, Zhisen & Yang, Zaili, 2021. "Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:reensy:v:206:y:2021:i:c:s0951832020307754
    DOI: 10.1016/j.ress.2020.107277
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    3. Fan, Lixian & Zhang, Meng & Yin, Jingbo & Zhang, Jinfen, 2022. "Impacts of dynamic inspection records on port state control efficiency using Bayesian network analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Jose Manuel Prieto & Victor Amor & Ignacio Turias & David Almorza & Francisco Piniella, 2021. "Evaluation of Paris MoU Maritime Inspections Using a STATIS Approach," Mathematics, MDPI, vol. 9(17), pages 1-13, August.
    5. Liu, Kezhong & Yu, Qing & Yang, Zhisen & Wan, Chengpeng & Yang, Zaili, 2022. "BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. Muhammad Reza Bagus & Shinya Hanaoka, 2023. "Interdependency patterns of potential seaport risk factors in relation to supply chain disruption in Indonesia," Journal of Shipping and Trade, Springer, vol. 8(1), pages 1-28, December.
    7. Yang, Zhisen & Yu, Qing & Yang, Zaili & Wan, Chengpeng, 2024. "A data-driven Bayesian model for evaluating the duration of detention of ships in PSC inspections," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    8. Xiao, Yi & Qi, Guanqiu & Jin, Mengjie & Yuen, Kum Fai & Chen, Zhuo & Li, Kevin X., 2021. "Efficiency of Port State Control inspection regimes: A comparative study," Transport Policy, Elsevier, vol. 106(C), pages 165-172.
    9. Zhu, Jiang-Hong & Yang, Qiang & Jiang, Jun, 2023. "Identifying crucial deficiency categories influencing ship detention: A method of combining cloud model and prospect theory," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Carine Dominguez-Péry & Rana Tassabehji & Franck Corset & Zainab Chreim, 2023. "A holistic view of maritime navigation accidents and risk indicators: examining IMO reports from 2011 to 2021," Journal of Shipping and Trade, Springer, vol. 8(1), pages 1-28, December.
    11. Yan, Ran & Mo, Haoyu & Guo, Xiaomeng & Yang, Ying & Wang, Shuaian, 2022. "Is port state control influenced by the COVID-19? Evidence from inspection data," Transport Policy, Elsevier, vol. 123(C), pages 82-103.

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