Analysis of Human and Organizational Factors Contributing to Vessel Collision Risks

The assessment of collision risks is crucial for effective risk control and scientific management of maritime safety. To prevent maritime transportation accidents, an accident causation model has been proposed to analyze risks in maritime transportation systems. The 24-model further analyzes the impact pathways of accident factors in the accident chain and calculates the fit of HOF-related factors. Using Bayesian Networks as a foundation and the 24-model as a tool, a Bayesian Network model for collision risk is constructed by identifying risk factors and determining their correlations, utilizing accident data from Chinese maritime authorities. Utilizing a Bayesian Network to construct a ship collision risk model that couples HOF and calculates conditional probabilities of relevant node occurrences. To explore the coupled relationships between nodes in a network, this study employs the N-K model to construct a safety risk coupling model for ship collision accidents, calculating risk values for different coupling types within the model. Case analysis shows that accidents result from dynamic interaction and linear combination of risk factors. The analysis of experimental results indicates that various accident factors contribute differently to overall maritime risk. Human factors are the direct cause of maritime ship collision accidents. From the perspective of coupled risk, organizational factors, as root influences, are crucial aspects that bridge resource management needs to focus on. The application of this model provides maritime personnel with a novel approach to mitigate the risk of maritime collisions.