Risk analysis of maritime accidents along the main route of the Maritime Silk Road: a Bayesian network approach

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

Acknowledgements

This work was supported by the National Natural Science Foundation of China [grant number 71974023], the Fundamental Research Funds for the Central Universities [grant number 313209302], the National Social Science Foundation of China [grant number 19VHQ012], and the China Scholarship Council (CSC).

Disclosure statement

No potential conflict of interest was reported by the authors

Additional information

Funding

This research was supported by the National Natural Science Foundation of China under Grant [71974023]; National Social Science Fund Major Project under Grant [18VHQ005]; and the Fundamental Research Funds for the Central Universities under Grant [3132016359].