Research on high-risk ship identification and collision risk control strategies based on AIS data

The rapid expansion of global maritime activities has led to a significant increase in ship traffic in busy waterways, resulting in frequent collisions that pose a serious threat to both shipping safety and the marine environment. Current risk assessment methods predominantly rely on static data, which hinders the effective capture of the dynamic influence of ship behavior on collision risks. Consequently, there is a pressing need for a risk quantification approach incorporating dynamic ship motion characteristics. This study, utilizing AIS (Automatic Identification System) data, proposes a risk quantification model that integrates ship kinematics and dynamic interaction behaviors. By analyzing dynamic parameters, including relative speed, position, and heading, the model constructs a spatiotemporal-driven dynamic collision boundary that identifies the closest collision points between ships, facilitating precise risk quantification in busy waterways. The study also proposes a collision control strategy that adjusts heading and speed, which is validated through simulation experiments. The results demonstrate that a combined strategy of large-angle turns and deceleration substantially reduces collision risk. This research offers a novel approach for identifying high-risk ships and optimizing collision avoidance strategies in busy waterways, with the potential for future expansion to address risk management needs in various waterway environments.