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Risk analysis of maritime accidents along the main route of the Maritime Silk Road: a Bayesian network approach
Authors:Meizhi Jiang  Zaili Yang  Jing Li
Institution:1. College of Transportation Engineering, Dalian Maritime University , Dalian, China ORCID Iconhttps://orcid.org/0000-0003-3244-8427;2. College of Transportation Engineering, Dalian Maritime University , Dalian, China;3. Liverpool Logistics, Offshore and Marine Research Institute, Liverpool John Moores University , Liverpool, UK ORCID Iconhttps://orcid.org/0000-0003-1385-493X;4. College of Transportation Engineering, Dalian Maritime University , Dalian, China
Abstract:ABSTRACT

The safety of maritime transportation along the twenty-first century Maritime Silk Road (MSR) is important to ensure its development and sustainability. Maritime transportation poses risks of accidents that can cause the death or injury of crew members and damage to ships and the environment. This paper proposes a Bayesian network (BN) based risk analysis approach that is newly applied in the main route of the MSR to analyse its relevant maritime accidents. The risk data are manually collected from the reports of the accident that occurred along the MSR. Next, the risk factors are identified and the results from the modelling method can provide useful insights for accident prevention. Historical data collected from accident reports are used to estimate the prior probabilities of the identified risk factors influencing the occurrence of maritime accidents. The results show that the main influencing factors are the type and location of an accident and the type, speed, and age of the involved ship(s). In addition, scenario analysis is conducted to analyse the risks of different ships in various navigational environments. The findings can be used to analyse the probability of each possible maritime accident along MSR and to provide useful insights for shipowners’ accident prevention.
Keywords:Maritime Silk Road  maritime risk  maritime safety  Bayesian network  sensitivity analysis  scenario analysis
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