Modelling collision risk between container and fishing ships during cross encounter in a cordon area

Liye Zhang, Kewang Gu, Zhicheng Ma, Bing Wu and Jie Song

Reliability Engineering and System Safety, 2025, vol. 264, issue PB

Abstract: Collisions between container ships and fishing ships frequently result in significant consequences, particularly in China. To assess the collision risk of ship cross-meetings in the warning area, this study constructed a collision risk model using historical AIS data. The risk influencing factors included relative distance, relative speed, relative bearing, distance closest point of approach (DCPA) and time closest point of approach (TCPA). We extracted pairs of trajectories of ships of different types (container ships and fishing ships) and employed the entropy approach to calculate the weights of each risk factor for the modelling of collisions between two ships in real time. Our findings indicate that the collision risk during a two-ship cross-meeting increases gradually as the meeting progresses, reaching a maximum value (approximately 0.66) at the point of nearest proximity, after which it decreases. A comparison and analysis of the three areas revealed that the warning area represented a high-risk area for the cross-meeting. In order to calculate the weight value of each ship and assess the real-time collision risk during the multi-ship cross-meeting, the Shapley value method was employed. The results indicate that the collision risk of a multi-ship encounter is significantly higher than that of a two-ship encounter. Furthermore, the change in risk values is more complex. This study not only quantifies the risk between two ships, but also provides a new method to quantify the risk of multiple ships. This provides important theoretical support for the safety of ship navigation.

Keywords: AIS data; Collision risk modelling; Multi-ship encounters; Shapely value method; Warning area (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:264:y:2025:i:pb:s0951832025005745

DOI: 10.1016/j.ress.2025.111373

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