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An operational risk analysis tool to analyze marine transportation in Arctic waters

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  • Khan, Bushra
  • Khan, Faisal
  • Veitch, Brian
  • Yang, Ming

Abstract

The Arctic Ocean has drawn major attention in recent years due to its rich natural resources and shorter navigational routes. Arctic development and transportation involve significant risk caused by the unique features of this region, such as ice, severe operating conditions, unpredictable climatic changes, and remoteness. Considering the high degree of uncertainty in the performance of vessel operating systems and humans, robust risk analysis and management tools are required to provide decision-support to prevent accidents and ensure safety at sea. This paper proposes an Object-Oriented Bayesian Network model to dynamically predict ship-ice collision probability based on navigational and operational system states, weather and ice conditions, and human error. The model, when integrated with potential consequences, may help estimate risk. A case study related to oil tanker navigation on the Northern Sea Route (NSR) is used to show the application of the proposed model to predict oil tanker collision with sea ice.

Suggested Citation

  • Khan, Bushra & Khan, Faisal & Veitch, Brian & Yang, Ming, 2018. "An operational risk analysis tool to analyze marine transportation in Arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 485-502.
    • Handle: RePEc:eee:reensy:v:169:y:2018:i:c:p:485-502
    DOI: 10.1016/j.ress.2017.09.014
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    References listed on IDEAS

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    17. Benz, Lukas & Münch, Christopher & Hartmann, Evi, 2021. "Fuzzy-based decision analysis on Arctic transportation: A guidance for freight shipping companies," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 375-400, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    18. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. Rajesh S. Prabhu Gaonkar & V. Mariappan, 2020. "Transportation time reliability appraisal in maritime context," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 736-746, June.
    20. 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.
    21. 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).
    22. Zhuang Li & Shenping Hu & Guoping Gao & Yongtao Xi & Shanshan Fu & Chenyang Yao, 2020. "Risk Reasoning from Factor Correlation of Maritime Traffic under Arctic Sea Ice Status Association with a Bayesian Belief Network," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    23. Li, Zhongping & Cui, Lirong & Chen, Jianhui, 2018. "Traffic accident modelling via self-exciting point processes," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 312-320.
    24. Tao, Longlong & Chen, Liwei & Ge, Daochuan & Yao, Yuantao & Ruan, Fang & Wu, Jie & Yu, Jie, 2022. "An integrated probabilistic risk assessment methodology for maritime transportation of spent nuclear fuel based on event tree and hydrodynamic model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).

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