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A novel data-driven method of ship collision risk evolution evaluation during real encounter situations

Jiongjiong Liu, Jinfen Zhang, Zaili Yang, Chengpeng Wan and Mingyang Zhang

Reliability Engineering and System Safety, 2024, vol. 249, issue C

Abstract: Aiming at realizing collision risk quantitative evaluation among encounter ships, a novel data-driven evolution model is proposed concerning encounter evolution in maritime transportation. A probabilistic velocity obstacle with an elliptic conflict region is constructed by taking into account uncertainty. The degree of and time to domain violation are introduced to quantify collision risk levels under varying velocities. Then, a ship collision risk evolution model is formulated by considering spatial attributes, macro-level and micro-level evolution based on a realistic collision avoidance decision. The model parameters and their weights are determined by statistical analysis of historical encounter scenarios and the characteristics of encounter stages. Therefore, the model encapsulates the statistical characteristics of actual data, which improves its practical values. The results of case studies indicate that the collision risk evolution model can properly reflect collision risk, so that collision evolution stages can be classified accordingly for rational anti-collision guidance. It brings new contributions to risk visualization, collision avoidance decision-making, and collision accident analysis and responsibility determination.

Keywords: Ship collision risk; Encounter evolution; Ship domain; Velocity obstacles; Automatic identification system (AIS); Maritime transportation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:249:y:2024:i:c:s0951832024003016

DOI: 10.1016/j.ress.2024.110228

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