## 1 Introduction  
Maritime transportation provides a guarantee for the spatial movement of goods worldwide due to its advantages, such as large transport capacity and low cost. As one of the main modes of transportation in international trade, maritime transport undertakes more than 90% of the global trade volume [1]. As global commerce continues to grow, water traffic, especially in coastal waters, has become increasingly busy and complex in recent years. The prosperity of waterway transportation has also increased the probability of water traffic accidents. To simplify traffic flow patterns in convergence areas and enhance navigational safety, ship routing is widely used [2]. The traffic separation scheme (TSS) is a specific type of ship routing that separates opposing traffic flows by employing appropriate methods and establishing traffic lanes. TSS has been widely developed around the world, and it has played a significant role in decreasing ship collision accidents and improving navigation safety. Chengshantou waters, a critical hub for China's north-south marine transportation, experience heavy ship traffic and occasional collision accidents. The implementation of a TSS in Chengshantou waters has improved navigational safety and navigational efficiency. However, despite the benefits of the TSS, collision accidents still occur due to navigators' failure to correctly understand and execute the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs) [3]. The optimal solution to human error is to improve the level of ship intelligence, which is also the hotspot and challenge of current related research [4]. In terms of current research, there is still a long way to go until fully autonomous navigation in restricted water and manual ship manipulation continues to be the primary method of ship manipulation. Intelligent maneuver decision-making, which complies with COLREGs and good seamanship [5; 6; 7], is currently the most promising strategy to reduce navigational risk. This approach also provides decision-making suggestions to navigators. The risks faced by ships during navigation, such as collisions, groundings, and rocks, as well as violations of TSS rules, are referred to as navigation risks in this study. Navigation risk warning and collision avoidance decision-making are the primary components of intelligent navigation [8]. Related research can reduce collision occurrences and lay the groundwork for intelligent navigation. Various methods, including collision avoidance mechanism [9; 10], four-stage theory [11; 12; 13], collision risk index (CRI) [12], velocity obstacle (VO) [14; 15; 16; 17], etc., have been widely applied in collision avoidance research. What is more, some academic research used deep reinforcement learning algorithms for ship collision avoidance decision-making, followed by intense training to obtain a basic collision avoidance model [18; 19]. Artificial potential fields [20; 21], ant colony algorithms [22], genetic algorithms [23], neural networks [24], and other intelligent algorithms also have significant influence when they are used to solve the collision avoidance path planning problem. At present, most of the research on special waters, such as ship routing waters, is to analyze the traffic characteristics [25] and optimize [26] the ship's routing scheme. For example, [PERSON] et al. [27] analyzed the impact of the TSS in the Sunda Strait in preventing ship accidents. He believed that the potential collision between ships could be minimized by using TSS to separate ships' traffic flow in the opposite direction. [PERSON] et al. [28] analyzed the ship routing system, navigation environment, and traffic flow in the Bohai Strait, optimized the shipping route in this water area, and proposed a recommended scheme to connect the existing routing system in this water area. [PERSON] et al. [29; 30] defined the ship domain within the precautionary area of TSS and investigated the law of traffic flow and ship behavior in the real environment.