Ship collision risk is a critical issue in maritime traffic safety regulation, as it directly affects the safety, efficiency, and sustainability of maritime transportation. It depends not only on the current encounter geometry among ships, but is also closely related to the ship trajectory distribution structure and the traffic state. Existing studies have mostly identified collision risk based on collision avoidance parameters. Although such methods can characterize explicit collision risks, they remain insufficient in identifying the additional risks induced by trajectory densification, uncovering the potential risks reflected by frequent trajectory intersection and change, and representing the structural collision risks of regional traffic. To address these limitations, this study proposes a trajectory analysis-based regional collision risk model within the framework of the radial distribution function. First, the mapping relationships between collision risk and three aspects, namely trajectory density, trajectory conflict, and trajectory abruptness, are established, which are respectively characterized by trajectory density and aggregation, trajectory intersections and time differences, and trajectory alterations and fluctuations. Then, the ship traffic system is transformed into a particle system, and two-dimensional radial distribution feature planes for the above three aspects are constructed to identify the risk level of a region from different dimensions. Finally, a three-dimensional fusion space is further developed to achieve a comprehensive quantification of collision risk in a specified water area. Experiments were conducted using one week of daytime and nighttime Automatic Identification System (AIS) data from the Bohai Strait. The proposed model showed a strong temporal correlation with AIS record-based high-risk patterns (R = 0.866, p = 0.01), and, compared with a regional collision risk model based on traditional collision avoidance parameters, exhibited 20–50% higher sensitivity in identifying additional and potential risks caused by dense, intersecting, and abrupt trajectory patterns. The proposed model can provide methodological support for maritime authorities in collision risk monitoring of key waters, precise allocation of regulatory resources, and proactive safety regulation, thereby contributing to safer and more sustainable maritime transportation.
Zhou et al. (Sat,) studied this question.