Bayesian networks for maritime traffic accident prevention: Benefits and challenges

Maria Hänninen

Research output: Contribution to journalArticleScientificpeer-review

151 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)305-312
Number of pages8
JournalAccident Analysis and Prevention
Publication statusPublished - Dec 2014
MoE publication typeA1 Journal article-refereed


  • Bayesian networks
  • Data quality
  • Expert knowledge
  • Maritime traffic safety
  • Ship accidents


Dive into the research topics of 'Bayesian networks for maritime traffic accident prevention: Benefits and challenges'. Together they form a unique fingerprint.

Cite this