Human and organizational factors analysis of collision accidents between merchant ships and fishing vessels based on HFACS-BN model

Abstract

Collision accidents between merchant ships and fishing vessels have attracted much attention owing to the relatively high frequency and serious consequences, at least in China. This paper proposes a hybrid methodology incorporating the Human Factors Analysis and Classification System and Bayesian networks to investigate the human and organizational factors of collision accidents between merchant ships and fishing vessels. The kernel of this model is to identify human and organizational factors using a modified Human Factors Analysis and Classification System framework based on 443 historical collision accidents from 2013 to 2023 in China, to transform relationships of five levels in the Human Factors Analysis and Classification System to the graphical structure of the Bayesian network and to apply Expectation Maximization algorithm for parameter learning to obtain the parameters of the Bayesian network. 56 relevant human and organizational factors are identified, including 11 special influencing factors related to collisions between merchant ships and fishing vessels. The proposed model is validated using two axioms, key influencing factors are identified through sensitivity analysis, and development paths of collision accidents are derived through the strength of influence analysis. Consequently, the findings of this study provide valuable insights for the Maritime Administration, fisheries supervisory agencies, shipping companies, fishermen, and other stakeholders in formulating effective strategies to prevent collision accidents between merchant ships and fishing vessels.