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BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data

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  • Liu, Kezhong
  • Yu, Qing
  • Yang, Zhisen
  • Wan, Chengpeng
  • Yang, Zaili

Abstract

Given to the increasing traffic volume in ports in recent years, ship selection and inspection procedure in the port state control (PSC) should be improved to reduce any unnecessary delay caused by the inefficient inspections. This study aims to newly use a data training technique and the newest PSC data to improve the usage of Bayesian Network (BN) to assess detention risk to a point where risk factors are identified, interrelationships among the factors are analysed and prior probability training based on big data is obtained more easily. To construct the BN model, a Bayesian theorem-based machine learning approach is adopted to ensure the obtained model is objective and reliable. The model is developed based on 1880 inspection records in the Paris Memorandum of Understanding (MoU) regime between 1st January 2017 and 31st March 2020. The obtained model not only present the probability distribution of each factor but also explore interrelationships among them. Compared to the Ship Risk Profiles (SRP) model, the used data-driven structure learning algorithm is more convenient and useful. The analysis results provide insights for ship owners to manage ship detention risk while support port authorities to prioritize the ship checklist and utilise more efficient ship inspection.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022001843
    DOI: 10.1016/j.ress.2022.108530
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    as
    1. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. 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.
    3. 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).
    4. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    5. 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).
    6. Wang, Shuaian & Yan, Ran & Qu, Xiaobo, 2019. "Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 129-157.
    7. 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.
    8. Shubo Wu & Xinqiang Chen & Chaojian Shi & Junjie Fu & Ying Yan & Shengzheng Wang, 2022. "Ship detention prediction via feature selection scheme and support vector machine (SVM)," Maritime Policy & Management, Taylor & Francis Journals, vol. 49(1), pages 140-153, January.
    9. Micro & Macro Marketing, 2018. "Reviewers 2017," Micro & Macro Marketing, Società editrice il Mulino, issue 1, pages 7-10.
    10. Yang, Zhisen & Wan, Chengpeng & Yang, Zaili & Yu, Qing, 2021. "Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    11. Wan, Chengpeng & Yan, Xinping & Zhang, Di & Qu, Zhuohua & Yang, Zaili, 2019. "An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 222-240.
    12. Rynarzewski T. & Szymczak M., 2018. "Book reviews," Economics and Business Review, Sciendo, vol. 4(2), pages 114-117, June.
    13. Yu, Qing & Liu, Kezhong & Chang, Chia-Hsun & Yang, Zaili, 2020. "Realising advanced risk assessment of vessel traffic flows near offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    14. A. G. Eleye‐Datubo & A. Wall & J. Wang, 2008. "Marine and Offshore Safety Assessment by Incorporative Risk Modeling in a Fuzzy‐Bayesian Network of an Induced Mass Assignment Paradigm," Risk Analysis, John Wiley & Sons, vol. 28(1), pages 95-112, February.
    15. Chang, Chia-Hsun & Kontovas, Christos & Yu, Qing & Yang, Zaili, 2021. "Risk assessment of the operations of maritime autonomous surface ships," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    16. Junjie Fu & Xinqiang Chen & Shubo Wu & Chaojian Shi & Huafeng Wu & Jiansen Zhao & Pengwen Xiong, 2020. "Mining ship deficiency correlations from historical port state control (PSC) inspection data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    17. ., 2018. "The peer-review process," Chapters, in: Publish or Perish, chapter 7, pages 119-137, Edward Elgar Publishing.
    18. Dandolo F., 2018. "A Review on Immigration Issues in Italy," Rivista economica del Mezzogiorno, Società editrice il Mulino, issue 1-2, pages 167-186.
    19. Gorazda M. & Hardt Ł. & Kwarciński T. & Gorynia Marian, 2018. "Book Reviews," Economics and Business Review, Sciendo, vol. 4(1), pages 107-110, April.
    20. Yang, Zhisen & Yang, Zaili & Teixeira, Angelo Palos, 2020. "Comparative analysis of the impact of new inspection regime on port state control inspection," Transport Policy, Elsevier, vol. 92(C), pages 65-80.
    21. Graziano, Armando & Mejia, Maximo Q. & Schröder-Hinrichs, Jens-Uwe, 2018. "Achievements and challenges on the implementation of the European Directive on Port State Control," Transport Policy, Elsevier, vol. 72(C), pages 97-108.
    22. 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.
    23. Dr. Kyriaki Mitroussi, 2003. "Third party ship management: the case of separation of ownership and management in the shipping context," Maritime Policy & Management, Taylor & Francis Journals, vol. 30(1), pages 77-90, January.
    24. Cariou, Pierre & Wolff, Francois-Charles, 2015. "Identifying substandard vessels through Port State Control inspections: A new methodology for Concentrated Inspection Campaigns," Marine Policy, Elsevier, vol. 60(C), pages 27-39.
    25. Jiang, Dan & Wu, Bing & Cheng, Zhiyou & Xue, Jie & van Gelder, P.H.A.J.M., 2021. "Towards a probabilistic model for estimation of grounding accidents in fluctuating backwater zone of the Three Gorges Reservoir," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    26. 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).
    27. Yu, Qing & Liu, Kezhong & Yang, Zhisen & Wang, Hongbo & Yang, Zaili, 2021. "Geometrical risk evaluation of the collisions between ships and offshore installations using rule-based Bayesian reasoning," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    28. 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.
    29. 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.
    30. Vrijendra, 2018. "Book Review: Of Disruptive Signals," Working Papers id:12369, eSocialSciences.
    31. Chengpeng Wan & Zaili Yang & Di Zhang & Xinping Yan & Shiqi Fan, 2018. "Resilience in transportation systems: a systematic review and future directions," Transport Reviews, Taylor & Francis Journals, vol. 38(4), pages 479-498, July.
    32. Hossain, Niamat Ullah Ibne & Nur, Farjana & Hosseini, Seyedmohsen & Jaradat, Raed & Marufuzzaman, Mohammad & Puryear, Stephen M., 2019. "A Bayesian network based approach for modeling and assessing resilience: A case study of a full service deep water port," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 378-396.
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