Highlights Pros and cons in applying Bayesian networks (BNs) to ship safety are discussed. Ship safety is a complex entity with uncertainty, and BNs can cope with such features. The option to combine data with expert views further enhance their feasibility. Expert elicitation might be challenging and the model validation can be tricky.
Abstract Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making.
Bayesian networks for maritime traffic accident prevention: Benefits and challenges
Accident Analysis and Prevention ; 73 ; 305-312
2014-09-13
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Bayesian networks for maritime traffic accident prevention: Benefits and challenges
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