## 2 Related Work
[14] proposed a novel probabilistic approach based on Bayesian networks to characterize the static risk of individual ships and maritime traffic. The developed model inferred regional maritime traffic risk profiles using incomplete information from the AIS data of ships. [PERSON] et al. [15] introduced a novel systematic multi-scale collision risk estimation method to capture traffic conflict patterns across various spatial scales. A ship traffic network was constructed to evaluate the collision risk of maritime traffic in specific water areas, quantifying interactions and dependencies among multiple ships. Additionally, the regional and global maritime traffic was partitioned based on spatiotemporal interrelations among ships, and collision risk assessments were performed for the generated traffic clusters. Furthermore, researchers are actively studying information visualization methods to effectively use maritime traffic information in traffic management, risk analysis, and route planning for risk areas identified based on AIS data. However, existing maritime traffic analysis and visualization studies focus solely on traffic density or pattern analysis, limiting their practical application in real-time navigation decisions. [PERSON] et al. [16] used spatial autocorrelation analysis methods to investigate whether near collisions exhibit spatial clustering from global and local perspectives. Based on the AIS data of maritime traffic off the coast of Portugal, the analysis identified several high-risk areas (hotspots) with high densities of near ship collisions. [PERSON] et al. [17] examined the empirical values of the bow crossing range (BCR) during ship operations and analyzed the factors influencing this indicator and their extent of impact. A large-scale AIS dataset from the Danish coast was analyzed, revealing that BCR values typically vary depending on the ship type and operational area. Additionally, the results were visualized as density maps through a hotspot map. [PERSON] et al. [18] proposed a procedure for analyzing and visualizing marine traffic density using AIS data, which is preprocessed to extract representative specific routes based on globally collected AIS data and visualized on a map as nonpoint routes. [PERSON] et al. [7] proposed a method for processing the movement of mobile objects such as ships, using kernel density estimation, multivariate trajectory data analysis, and composite density maps. Particularly, they combined multiple density fields to generate composite density maps, enabling detailed analysis of complex patterns, such as collision risk, traffic flow, and danger zones. [PERSON], [PERSON] [19] used a framework to analyze, simulate, and visualize maritime traffic data in the Aegean Sea. The visualization process clustered data based on parameters, such as vessel density, speed, and cargo type, to identify risk areas, which were then color-coded on a map. Clustering algorithms, such as k-means, expectation maximization, and density-based spatial clustering of applications with noise (DBSCAN), were applied to group similar behaviors, and risk levels were visualized using a color gradient, with safe areas in transparent colors and dangerous areas in dark red. [PERSON] [20] used AIS data to effectively visualize and analyze vessel movement patterns and maritime activity density. The AIS point data were converted into a grid to create monthly density maps, with contrast adjustment applied for route density visualization. [PERSON] [21] analyzed data from automatic ship reporting systems such as AIS, long-range identification and tracking (LRIT), and vessel monitoring system (VMS) and developed a method for visualizing the location and situation of ships (maritime situational picture (MSP)). Data from specific sea areas were visualized as a ship density map by month, with the density displayed as a gradient color in a grid. [PERSON] [22] identified near-miss areas between ships and visualized them using density maps.