IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v181y2024ics1366554523003599.html
   My bibliography  Save this article

A data-driven Bayesian model for evaluating the duration of detention of ships in PSC inspections

Author

Listed:
  • Yang, Zhisen
  • Yu, Qing
  • Yang, Zaili
  • Wan, Chengpeng

Abstract

Port State Control (PSC) inspections are essential for port authorities to improve vessel quality and ensure maritime safety worldwide. However, the increasing frequency and duration of ship detentions indicate serious deficiencies of visiting vessels still largely exist, highlighting the urgent need for scientific solutions. This research aims to improve the efficiency of inspection policy and reduce the duration of detention by developing a data-driven Bayesian Network (BN) model using an improved machine-learning (ML) based methodology. New risk variables influencing the duration of ship detention, especially deficiency types, are identified based on the established database containing detention records within the jurisdiction of the Paris MoU from January 2015 to March 2022. Thorough analysis using the developed model allows the identification of deficiency types with a significant impact on the duration of detention, the discovery of interdependencies between these types and the clarification of the major and abnormal deficiency types in different port states. Policy implications and managerial recommendations for port authorities are presented. These include developing clear instructions on types of deficiencies that significantly impact detention time and proposing a selection strategy for vessels in different countries based on their specific circumstances. The proposed model utilizes big data analytics to support the development of inspection policies that are rational and effective. This research will provide good reference for effectively reducing the duration of ship detention, providing policy recommendations, improving ship standards, and ensuring maritime safety.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:transe:v:181:y:2024:i:c:s1366554523003599
    DOI: 10.1016/j.tre.2023.103371
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554523003599
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tre.2023.103371?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wan, Chengpeng & Yan, Xinping & Zhang, Di & Yang, Zaili, 2019. "A novel policy making aid model for the development of LNG fuelled ships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 29-44.
    2. Peter W. J. Batey & Geoffrey J. D. Hewings, 2021. "Demo-economic Modeling: Review and Prospects," International Regional Science Review, , vol. 44(3-4), pages 328-362, May.
    3. Vander Hoorn, Stephen & Knapp, Sabine, 2015. "A multi-layered risk exposure assessment approach for the shipping industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 21-33.
    4. Baydaa Hassan Husain & Subhi R. M. Zeebaree, 2021. "Improvised Distributions framework of Hadoop: A review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 31-41.
    5. 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.
    6. 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).
    7. 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).
    8. Zhang, Liye & Meng, Qiang & Fang Fwa, Tien, 2019. "Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 287-304.
    9. Yan, Ran & Wang, Shuaian & Zhen, Lu, 2023. "An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    10. 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).
    11. Xiaoying Ma, 2021. "Review of Past Literature," Springer Books, in: The Economic Impact of Government Policy on China’s Private Higher Education Sector, chapter 0, pages 9-30, Springer.
    12. Thilagavathy S. & Geetha S.N., 2021. "Work-life balance -a systematic review," Vilakshan - XIMB Journal of Management, Emerald Group Publishing Limited, vol. 20(2), pages 258-276, December.
    13. Karsten, Christian Vad & Brouer, Berit Dangaard & Desaulniers, Guy & Pisinger, David, 2017. "Time constrained liner shipping network design," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 152-162.
    14. v, Eric, 2021. "Review Sitasi Pemikiran dan Bisnis," OSF Preprints fj9dz, Center for Open Science.
    15. 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).
    16. 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).
    17. 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.
    18. Fan, Lixian & Luo, Meifeng & Yin, Jinbo, 2014. "Flag choice and Port State Control inspections—Empirical evidence using a simultaneous model," Transport Policy, Elsevier, vol. 35(C), pages 350-357.
    19. Maximilian Lantelme & Laura K. C. Seibold & Hermut Kormann, 2021. "The Issue of Required Growth: A Literature Review," Springer Books, in: German Family Enterprises, edition 2, chapter 0, pages 91-108, Springer.
    20. Benz, Lukas & Münch, Christopher & Hartmann, Evi, 2021. "Development of a search and rescue framework for maritime freight shipping in the Arctic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 54-69.
    21. editors, 2021. "List Of Reviewers - 2020," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 67(1), pages 1-1.
    22. Bai, Xiwen & Xu, Ming & Han, Tingting & Yang, Dong, 2022. "Quantifying the impact of pandemic lockdown policies on global port calls," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 224-241.
    23. Knapp, Sabine & Franses, Philip Hans, 2007. "Econometric analysis on the effect of port state control inspections on the probability of casualty: Can targeting of substandard ships for inspections be improved?," Marine Policy, Elsevier, vol. 31(4), pages 550-563, July.
    24. Kosrat Dlshad Ahmed & Subhi R. M. Zeebaree, 2021. "Resource Allocation in Fog Computing: A Review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 54-63.
    25. Yang, Zaili & Ng, Adolf K.Y. & Wang, Jin, 2014. "A new risk quantification approach in port facility security assessment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 72-90.
    26. Micro & Macro Marketing, 2021. "Reviewers 2020," Micro & Macro Marketing, Società editrice il Mulino, issue 1, pages 9-10.
    27. 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.
    28. 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.
    29. 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.
    30. Asadabadi, Ali & Miller-Hooks, Elise, 2020. "Maritime port network resiliency and reliability through co-opetition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    31. 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.
    32. 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).
    33. 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.
    34. 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.
    35. Yan, Ran & Wang, Shuaian & Cao, Jiannong & Sun, Defeng, 2021. "Shipping Domain Knowledge Informed Prediction and Optimization in Port State Control," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 52-78.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    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. 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).
    5. 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).
    6. 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).
    7. 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.
    8. Yan, Ran & Wang, Shuaian & Zhen, Lu, 2023. "An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    9. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. 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).
    11. Xuecheng Tian & Shuaian Wang, 2022. "Cost-Sensitive Laplacian Logistic Regression for Ship Detention Prediction," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    12. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Antão, P. & Sun, S. & Teixeira, A.P. & Guedes Soares, C., 2023. "Quantitative assessment of ship collision risk influencing factors from worldwide accident and fleet data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    14. 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.
    15. Yan, Ran & Wang, Shuaian & Fagerholt, Kjetil, 2020. "A semi-“smart predict then optimize” (semi-SPO) method for efficient ship inspection," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 100-125.
    16. 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.
    17. Yan, Ran & Wang, Shuaian & Cao, Jiannong & Sun, Defeng, 2021. "Shipping Domain Knowledge Informed Prediction and Optimization in Port State Control," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 52-78.
    18. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "A Decision-Focused Learning Framework for Vessel Selection Problem," Mathematics, MDPI, vol. 11(16), pages 1-13, August.
    19. Tian, Xuecheng & Yan, Ran & Liu, Yannick & Wang, Shuaian, 2023. "A smart predict-then-optimize method for targeted and cost-effective maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 32-52.
    20. Yan, Ran & Liu, Yan & Wang, Shuaian, 2024. "A data-driven optimization approach to improving maritime transport efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:181:y:2024:i:c:s1366554523003599. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.