## 5 Conclusions  
Hydrographic information is essential for navigation and route determination and has been steadily improving over time. Maritime traffic information of a specific sea area is typically visualized through electronic nautical charts along with the hydrographic information. Most studies related to the display of maritime traffic information have analyzed traffic density and flow separately or visualized them as cumulative data. This study proposes a method to integrate and visualize traffic density, maritime traffic direction, and traffic flow identified through unsupervised learning. It investigates the integration of vessel traffic behavior visualization using AIS dynamic data to propose an integrated visualization display for navigators' decision-making. By combining maritime traffic data and time-series dynamic ship navigation data, this study develops a system that simultaneously displays these metrics. Additionally, it helps ship operators and traffic managers to understand maritime traffic trends more comprehensively and make effective decisions. In this study, ship traffic behavior was divided into three categories: maritime traffic density, traffic direction, and traffic flow. First, the traffic density was divided into grid levels to visualize the traffic density information of ships as heat maps. Second, the traffic flow of ships was classified using a clustering technique during unsupervised learning, where the traffic direction information of ships was identified as traffic azimuth arrows using the traffic density's grid level. Third, based on the direction of the classified vessels, the traffic flow information of the vessels in the sea area was displayed, and representative routes and waypoints were identified. Finally, a visualization of maritime traffic density, traffic direction, and traffic flow was displayed simultaneously to provide situational awareness and decision support to ship operators sailing in the target area by overlaying the visualization of hydrographic information. Additionally, using the developed method, two entrance and exit routes of Mokpo Port in South Korea were selected as simulated waters. The visualization results were overlaid on an S-57-based electronic nautical chart. Through the simulation results, the density and direction of maritime traffic in the target area were evaluated using a grid system, identifying representative traffic flows and extracting waypoints. This study categorizes maritime traffic behaviors into three types, namely traffic density, direction, and flow, and proposes a grid-based visualization method to display these behaviors simultaneously, unlike previous studies. However, a limitation of this study is the inconsistency in grid levels for traffic density and direction, which may distort information. Further research is required on the real-time application of large AIS datasets to display traffic behaviors and a detailed input system procedure for applying maritime traffic behaviors in an actual hydrographic information system. The proposed method enables users to visualize traffic density, direction, and flow alongside hydrographic data, providing clearer and more intuitive insights. Additionally, future research will focus on defining maritime traffic behavior in the next-generation hydrographic information standard (S-100) system to enhance decision support for ship operators and controllers. Conceptualization, D.L. and D.J. ; methodology, D.J. ; software, D.L. and D.J. ; validation, D.L. and D.J. ; formal analysis, D.J. ; investigation, S.Y. and D.L. ; resources, D.L. ; data curation, S.Y. and D.L. ; writing--original draft preparation, D.L. ; writing--review and editing, D.J. ; visualization, D.L. and D.J. ; supervision, D.J. ; project administration, D.J. ; funding acquisition, S.Y. and D.J. All authors have read and agreed to the published version of the manuscript.