A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection

Port state control (PSC) inspections are deemed as an effective way to detect substandard vessels and ensure maritime safety around the world. Despite great efforts on PSC in recent years, one challenge that still exists in today’s PSC inspection practice is that there lack relevant scheme or academic research focusing on the duration of vessel detention, which is of great importance to the inspection system. To assist port authorities in estimating detention duration and minimizing the existence of substandard vessels, this paper aims to develop a novel data-driven machine learning based model based on the inspection records collected within the jurisdiction of Paris MoU from January 2015 to March 2022. The model is trained via the incorporation of an Improved Tree Augmented Naïve (ITAN) learning approach and a maximum a posteriori probability (MAP) of Expectation Maximization (EM) approach for the first time within the context of PSC research, which could be used as a prediction tool to determine rational durations for detained vessels. Thorough analysis of the proposed model enables the identification of risk variables and deficiency types having significant effects leading to long duration of detention. Further, the research findings could reveal managerial suggestions and insights for port authorities to reduce the occurrence of substandard vessels via the inspection system, i.e., identify specific risk level of vessels and ensure a more-efficient vessel selection process; design specific instructions and rules to regulate risk variables and deficiencies with huge effect on a long duration of detention. This research will provide insightful reference for effectively improving vessel quality, inspection efficiency, and maritime safety.