## 5 Conclusions  
The advantage of the DBSCAN algorithm in identifying noise clusters was observed in the results of the DBSCAN algorithm proposed in this study. Although noise clusters cannot be definitively classified as abnormal ship routes, routes classified as noise clusters indicate that they do not use the main routes or customary routes in the region. This information can allow operators and controllers to observe ship movements closely.  
Additionally, through the sub-route clustering process, the final result shows sub-clusters, such as Cluster 1-1 and Cluster 1-2, which differentiate the departure and destination points, and Cluster 5-1 and Cluster 5-2, which are divided into southern and northern routes based on obstacles in the middle of the route. Furthermore, Clusters 2-1 and 2-2 identified routes used not only by fishing boats and coastal passenger ships but also by large passenger ships to shorten travel time, whereas Clusters 3-1-1 and Cluster 4 identified the routes of coastal passenger ships; this allows for the identification of not only the main routes but also the segmentation of routes, enabling the exploration of customary routes in the region. The key feature of the ship route clustering algorithm proposed in this study is the suggestion and application of a criterion to automatically determine the parameters of the DBSCAN clustering algorithm, as well as the proposal and application of a re-analysis and termination criterion for clustering analysis through the sub-route clustering process for route segmentation.  
In this study, the number of trajectory points per ship was arbitrarily set to 50 during preprocessing. However, further research is needed to determine the most suitable number of points, considering the study area's scope, the data volume, and the data processing speed. In addition, research involving various simulations--such as high density vs. low density, simple patterns, and small-scale vs. large-scale analyses--is planned.  
Building on the ship route clustering methodology utilized in this study, future research can be expanded to explore the changes and impacts on route selection under different weather conditions through integrated studies using meteorological data. It can also extend to proposing optimal routes based on different ship types and cargo characteristics. Furthermore, our research is expected to contribute to the autonomous and intelligent navigation of MASS, a future game-changer in the maritime industry. It could also aid in the development of environmentally friendly routes and operational strategies, ultimately leading to the design of sustainable maritime traffic systems.  
Conceptualization, D.-H.L. and J.-S.K.; methodology, J.-S.K.; software, D.-H.L. and J.-S.K.; validation, D.-H.L. and J.-S.K.; formal analysis, J.-S.K.; investigation, D.-H.L.; resources, D.-H.L.; data curation, D.-H.L.; writing--original draft preparation, D.-H.L.; writing--review and editing, J.-S.K.; visualization, D.-H.L.; supervision, J.-S.K.; project administration, J.-S.K.; funding acquisition, J.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Marine Science and Technology Promotion (KIMST), funded by the Ministry of Oceans and Fisheries (RS-2023-00238653), and by the Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (MOE) (2023 RIS-009).  
Not applicable.  
All data and materials are available on request from the corresponding author. The data are not publicly available due to ongoing researches using a part of the data.  
The authors declare no conflict of interest.