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Effectiveness of port state control inspection using Bayesian network modelling

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  • Lixian Fan
  • Lan Zheng
  • Meifeng Luo

Abstract

As a key factor in ship safety, the effectiveness of the Port State Control (PSC) is very important. This study investigates the effect of the PSC inspection on ship accident and its impacts on the ship safety level in the next inspection, together with the ship inherent attributes and the time interval between inspections. The ship safety level is judged by the number of defects found in the PSC inspection and the risk of ship accident in this study. The Bayesian Network (BN) model is employed and the Bayesian Search algorithm is used to learn the structural networks using the data from various data sources. In addition, the safety level of ships at different time is also introduced into the model as latent variables. The results suggest that the safety level in the first inspection has a significant impact on the inspection time interval and the safety level of the next inspection. It is optimal to select vessels with a medium inspection time interval for inspection to improve ship safety quality effectively. This model can not only help to detect and monitor the dynamic changes in the effectiveness of PSC inspection, but also improve the PSC inspection system to provide guidance for stakeholders.

Suggested Citation

  • Lixian Fan & Lan Zheng & Meifeng Luo, 2022. "Effectiveness of port state control inspection using Bayesian network modelling," Maritime Policy & Management, Taylor & Francis Journals, vol. 49(2), pages 261-278, February.
    • Handle: RePEc:taf:marpmg:v:49:y:2022:i:2:p:261-278
    DOI: 10.1080/03088839.2020.1841913
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    Cited by:

    1. 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).
    2. Yang, Zhisen & Wan, Chengpeng & Yu, Qing & Yin, Jingbo & Yang, Zaili, 2023. "A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).

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