In this article, a ship manoeuvrability-based simulation for ship navigation in collision situations is established. Under the general requirement from the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) and good seamanship, the determination of encounter situations is quantified to reduce navigators’ intervention. Meanwhile, the action manner by course alteration or changing speed in some typical encounter situations is graphically analysed for both the give-way and stand-on vessels. Then, the multiple genetic algorithm and linear extension algorithm are adopted to perform trajectory planning for collision avoidance. To improve the reliability of the simulation system, the mathematical model of ship motion and ship manoeuvring control mechanism are adopted, which can eliminate the insufficiency of neglect of ship manoeuvrability in the process of collision avoidance. Meanwhile, the course encoding technique is adopted to fit the ship manoeuvring control mechanism. Finally, a set of traffic scenarios emulating different encounter situations are applied to demonstrate the effectiveness, consistency, and practicality of this system.

Regional collision risk identification and prediction is important for traffic surveillance in maritime transportation. This study proposes a framework of real-time prediction for regional collision risk by combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique, Shapley value method and Recurrent Neural Network (RNN). Firstly, the DBSCAN technique is applied to cluster vessels in specific sea area. Then the regional collision risk is quantified by calculating the contribution of each vessel and each cluster with Shapley value method. Afterwards, the optimized RNN method is employed to predict the regional collision risk of specific seas in short time. As a result, the framework is able to determine and forecast the regional collision risk precisely. At last, a case study is carried out with actual Automatic Identification System (AIS) data, the results show that the proposed framework is an effective tool for regional collision risk identification and prediction.