Analysis of Influencing Factors on the Severity of Ship Collision Accidents Based on an Improved TAN-BN

This study proposes an improved tree-augmented Bayesian network (TAN-BN) method for analyzing the severity of ship collision accidents by introducing the information contribution rate (ICR) for edge orientation and flexible filtering constraints for structure optimization. Based on 634 ship collision accident reports, a Bayesian network covering accident attributes and causal factors was constructed. The results show that the improved model achieved an overall AUC of 0.864, higher than that of the traditional TAN model (0.827). Mutual information analysis identified ship length as the factor most strongly associated with accident severity, with a mutual information value of 0.0868. Sensitivity analysis based on true risk impact (TRI) further showed that ship length, time, and ship type were the most influential factors, with average TRI values of 19.4%, 8.8%, and 7.2%, respectively. The proposed model effectively captures the dependency relationships between accident severity and multiple influencing factors and can provide quantitative support for risk warning and accident prevention in maritime traffic safety.