Impact analysis of external factors on human errors using the ARBN method based on small-sample ship collision records.

In this study, an Association Rule Bayesian Networks (ARBN) method is established to investigate the impacts of external factors (i.e. environmental factors and ship factors) on human errors. The Bayesian networks have been structured by mining association rules from historical collision accident records. In addition, unlike other related studies that have introduced subjective data (i.e. expert knowledge), this study tends to build Bayesian networks with only objective data (i.e. accident data). In order to solve the problem of small sample size during conditional probability table estimating, this study constructs an environmental-human Bayesian network and a ship-human Bayesian network separately. The results reveal that visibility, location, and time of the day are the main environmental factors affecting the occurrence probability of human errors. For more complex scenarios, the probability of occurring judgment/operation errors in ship collision is 83% under the evidence of spring, daytime, and sea area. Among the ship factors, gross tonnage, ship types, and over safe speed show significant effects on occurring human errors. Large ships are more likely to occur judgment/operation errors under the high-speed situation. From the consequence perspective, negligence errors are highly associated with severe collisions. • This study investigates the occurrence likelihood of human errors under different scenarios. • An ARBN method is developed to reflect the relationship using 10 years' collision accident records. • This study constructs concise ARBN models separately according to the types of influencing factors. • The probability of occurring judgment/operation errors is 83% for ship collisions occurring in spring, daytime and sea area. [ABSTRACT FROM AUTHOR]