Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network

Abstract

A data-driven Bayesian network (BN) is used to investigate the effect of human factors on maritime safety through maritime accident analysis. Its novelties consist of (1) manual collection and analysis of the primary data representing frequencies of risk factors directly derived from maritime accident reports, (2) incorporation of human factors into causational analysis with respect to different maritime accident types, and (3) modelling by a historical accident data-driven approach, to generate new insights on critical human factors contributing to different types of accidents. The modelling of the interdependency among the risk influencing factors is structured by Tree Augmented Network (TAN), and validated by both sensitivity analysis and past accident records. Our findings reveal that the critical risk factors for all accident types are ship age, ship operation, voyage segment, information, and vessel condition. More importantly, the findings also present the differentiation among the vital human factors against different types of accidents. Most probable explanation (MPE) is used to provide a specific scenario in which the beliefs are upheld, observing the most probable configuration. The work pioneers the analysis of various impacts of human factors on different maritime accident types. It helps provide specific recommendations for the prevention of a particular type of accidents involving human errors.