Ship selection in port state control: status and perspectives

ABSTRACT

Port state control (PSC) inspection acts as a safeguard against maritime accidents and marine environment pollution. Due to limited inspection resources and high inspection costs, port states can only select substandard ships with high risk for inspection. Therefore, efficient and accurate identification of substandard ships is important. This study reviews the current ship selection methods used in different ports and proposed in the existing literature, then discusses their advantages and disadvantages. Based on this review, a combined model for ship risk prediction considering ship deficiencies and detention is developed and validated in this study. Reasonable and comprehensive comparisons of the proposed combined model and the current ship selection method at the Port of Hong Kong are conducted. The comparison results provide managerial insights and suggestions for Memorandum of Understandings (MoUs). This study is the first to review the ship selection methods implemented in port states and proposed in the PSC inspection literature. It is also the first study to combine the number of ship deficiencies and the probability of detention in a unified model to calculate ship risk. This study is valuable for improving the efficiency of ship selection in MoUs and thus protecting maritime transport.

KEYWORDS:

• Port state control
• machine learning in maritime transportation
• ship selection
• ship deficiency
• ship detention

Acknowledgments

We thank the editors and three anonymous reviewers for their insightful and constructive comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant Nos. 72071173, 71831008] and the Policy Innovation and Co-ordination Office (PICO) of the Government of the HKSAR [Project No. 2020.A6.148.20A];Policy Innovation and Co-ordination Office (PICO) of the Government of the HKSAR [2020.A6.148.20A];