A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system

Yang Liu, Xiaoxue Ma, Weiliang Qiao, Laihao Ma and Bing Han

Reliability Engineering and System Safety, 2024, vol. 241, issue C

Abstract: Disruption cognition is critical for the development of resilient maritime transportation systems to withstand uncertain risks and achieve sustainable development. Aiming at improving the resilience of maritime transportation systems, a comprehensive methodology is proposed in the present study to model the propagation process of disruptions. First, a conceptual framework of disruption propagation within resilience theory is developed for the maritime transportation system, based on which a directed weighted complex network of disruption propagation is established, and data-driven Bayesian inference is applied to extend the complex network using a probability-based method. The propagation process and mechanisms can then be analyzed quantitatively through critical node identification for each propagation stage and the determination of the shortest propagative paths by the combination of bidirectional Bayesian inference, sensitivity analysis, and uncertainty analysis. Then, the proposed methodology is applied to the Arctic maritime transportation system to improve resilience by controlling the key disruptions in each propagation stage and cutting off the critical disruption propagation paths. The findings suggest that greater effort should be devoted to strengthening the resilience aspects related to environmental forecast and route planning systems, monitoring and functional maintenance mechanisms, emergency responses pertaining to repair and damage control, emergency escape and evacuation, and coastal SAR services to reduce the escalated impact of disruption propagation.

Keywords: Disruption propagation; Resilience engineering; Arctic maritime transportation system; Complex network; Data-driven Bayesian network (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005343

DOI: 10.1016/j.ress.2023.109620

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