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Analysis of maritime transport accidents using Bayesian networks

Fan, S, Yang, Z, Blanco-Davis, E, Zhang, J and Yan, X (2020) Analysis of maritime transport accidents using Bayesian networks. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. ISSN 1748-006X

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A Bayesian network–based risk analysis approach is proposed to analyse the risk factors influencing maritime transport accidents. Comparing with previous studies in the relevant literature, it reveals new features including (1) new primary data directly derived from maritime accident records by two major databanks Marine Accident Investigation Branch and Transportation Safety Board of Canada from 2012 to 2017, (2) rational classification of the factors with respect to each of the major types of maritime accidents for effective prevention, and (3) quantification of the extent to which different combinations of the factors influence each accident type. The network modelling the interdependency among the risk factors is constructed by using a naïve Bayesian network and validated by sensitivity analysis. The results reveal that the common risk factors among different types of accidents are ship operation, voyage segment, ship type, gross tonnage, hull type, and information. Scenario analysis is conducted to predict the occurrence likelihood of different types of accidents under various situations. The findings provide transport authorities and ship owners with useful insights for maritime accident prevention.

Item Type: Article
Uncontrolled Keywords: 0904 Chemical Engineering, 0905 Civil Engineering, 0911 Maritime Engineering
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
H Social Sciences > HE Transportation and Communications
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: Sage Publications
Related URLs:
Date Deposited: 17 Mar 2020 12:15
Last Modified: 04 Sep 2021 07:40
DOI or ID number: 10.1177/1748006X19900850
URI: https://researchonline.ljmu.ac.uk/id/eprint/12523
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