## 2 Related Work  
Maritime traffic information refers to data derived from analyzing information on (i) vessel operations, such as vessel position, course, speed, and route; (ii) waterways and sea routes, such as ocean depth, topography, and geology; (iii) vessel traffic, such as vessel traffic, maritime accidents, and navigational warnings; and (iv) marine observations, such as currents, tides, and marine weather. These elements are crucial for effective management of maritime traffic. Additionally, maritime traffic behavior focuses on analyzing long-term vessel patterns and identifying recurring routes, major routes, and vessel behavior in specific waters. For example, a traffic behavior analysis may reveal areas of reduced vessel speed on a particular route or periodic changes in route choice. This analysis offers deeper insights into vessel movement mechanisms. Understanding maritime traffic behavior from traffic information requires an in-depth analysis and identification of factors such as traffic density, flow, and vessel movement patterns [7]. Visualization processing is crucial in effectively displaying the results of these analyses, providing an intuitive understanding of maritime traffic behavior. Additionally, this approach transforms complex datasets into map-based visuals, enabling the visualization of vessel density, traffic flow, and hazardous areas and ultimately enhancing efficient maritime traffic management and decision-making. Hydrographic information has provided traditional decision-support information to navigators, helping them navigate safely and understand the marine environment's characteristics [8]. Traditional hydrographic charts provide information such as route types and designations adopted by the International Maritime Organization (IMO) [9], marine area information for port access, coastal navigation, and representative routes and distances between major ports, often accompanied by visuals. Paper and electronic charts also show the physical characteristics of oceans, coastal areas, lakes, and rivers, including depth, coastline features, tides, currents, and underwater topography [10]. The IHO S-100 is a next-generation e-navigation standard that provides a technical structure for efficiently integrating and visualizing diverse marine data. This standard will potentially be crucial in enhancing the situational awareness of navigators and supporting safe and efficient decision-making. Consequently, future hydrographic information systems should adopt new approaches for visualizing and analyzing maritime traffic behavior. Recent research has focused on decision-support methods utilizing maritime traffic data, which are not provided by hydrographic information. Traffic data analysis, such as examining density, flow, and representative routes, helps identify traffic patterns and trends that traditional hydrographic information cannot provide. This approach enables navigators and maritime traffic managers to plan detailed voyages and identify hazardous areas. [PERSON] et al. [11] used a predictive analysis method to estimate the complexity of maritime traffic flow in inland waterways based on AIS data. Their proposed method quantified the complexity of traffic flow in inland waterways and analyzed the correlation between traffic flow complexity and the number of maritime accidents. [PERSON] et al. [12]proposed a novel systematic maritime traffic clustering approach to adaptively capture high-risk multi-vessel encounters. By using AIS data from the Ningbo-Zhoushan Port, they effectively identified high-risk/high-density clusters with reliability and validity, providing insights into areas requiring enhanced maritime surveillance and risk warnings. [PERSON] et al. [13] investigated the movement behavior of ships in narrow waterways using AIS data and presented the distribution of flow speeds and ship densities, along with their relationships to high-risk areas. [PERSON] et al.