The efficiency improvement of port state control based on ship accident Bayesian networks

Ship accident has always been the focus in shipping and it is concerned by port state authorities. This study tries to investigate the impact of various factors on ship accident along with the port state's detected deficiency items. Very importantly, it manages to identify the structural connections between the checked deficiency items. The data used in this study are mainly from Lloyd's register of shipping, International Maritime Organization and Tokyo Memorandum of Understanding, with a total of 64,847 observations obtained. The Bayesian network model is employed and the Greedy thick thinning and Bayesian search algorithms are used to learn the structural networks. In addition to the impacts of the deficiency items and the ship inherent attributes on ship accidents, this study identifies some key deficiency items for port states. It also analyzes the intense connections between the key deficiency items with others. This helps simplify the port state's inspection procedure and improve operational efficiency.