Data-driven Analysis on the Causal Chain of Waterborne Traffic Accidents: a hybrid framework based on the improved HFACS and Bayesian Network

In the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, the occurrence of waterborne traffic accidents poses a severe threat to life, property safety, and the environment. To gain a deeper understanding of the causal mechanisms behind waterborne traffic accidents, this study conducts a data-driven analysis on the causal chain of waterborne traffic accidents. By constructing a hybrid framework integrating the improved HFACS (Human Factors Analysis and Classification System) with Bayesian Network model, the study conducts a multi-dimensional analysis of accident causes. The constructed model is quantitatively analyzed using genie software by the accident samples collected from China MSA. Results indicate that there are 12, 3, 6, 2, 4, and 7 causal chains leading to collisions, contacts, fire/explosion, windstorms, sinking, and other types of accidents respectively. The research results can provide a reference for the enhancement on the safe operation of waterborne transportation.