## 4 Conclusions  
VTS systems and VTSOs are known for their contribution to the reduction in marine accidents. Nevertheless, detecting all traffic conditions in a timely manner during peak hours when vessels are densely populated is virtually impossible, even with additional operating methods, such as dual surveillance and central control surveillance for VTSOs. This study proposed the use of Pindex, the VTS traffic management priority index, to support decision making by extracting the priority of vessel traffic surveillance for VTSO. A simulation was performed using navigation data collected from the Mokpo Harbor and its adjacent waterways in South Korea. Consequently, the target sea area was clustered into 17 clustering IDs. After extracting the priority index from each ID, an integrated index was presented for the entire target sea area. By providing a quantified priority index that classifies CPA and TCPA into four levels of danger, threat, caution, and attention according to traffic conditions, VTSOs were visualized to recognize the situation in an intuitive manner. Ten high-risk group vessel pairs were extracted from the entire sea area and eighteen VTSOs were used to verify the effectiveness of the proposed Pindex. The VTSOs failed to search for an average of 4.1 ship pairs within the time limit without Pindex, whereas all vessel pairs were successfully searched with Pindex. In addition, the average search time of the VTSO group with the Pindex provided was 10.4 s, shortened from the average of 14.3 s for the VTSO group without the Pindex provided. Thus, through simulations for VTSOs to verify the effectiveness of the proposed Pindex, the VTSOs successfully searched all vessel pairs with the provided Pindex within time limitations. The results indicate that the proposed methods can overcome the problems identified in existing VTS systems: (i) surveillance failure due to situational awareness timeout by providing a data list of CPA and TCPA for all ship pairs, and (ii) the calculation of the risk index using fixed CPA and TCPA levels or ship safety domains without considering regional factors and characteristics. However, as the results of this study were obtained through an experiment by selecting a specific simulation area and period, its applicability to all sea areas and periods cannot be stated. In addition, the presented cluster chart has a limitation in that it is the result of a single slide. Furthermore, the data structure was not classified according to the size or type of the vessel, and the simulations were performed with all vessels that are required to participate in VTS regulation mandatorily. In future research, data analysis should be performed considering various factors, such as the segmentation of the dataset reflecting the type and size of the vessel and the environment with big data, to ensure that it can be applied to all sea areas. In addition, it is necessary to ensure the accuracy of the extracted priority index by reflecting the route prediction, including the coordinates with traffic patterns and the time required.  
Conceptualization, L.-n.L. and J.-s.K.; methodology, J.-s.K.; software, L.-n.L. and J.-s.K.; validation, L.-n.L. and J.-s.K.; formal analysis, J.-s.K.; investigation, L.-n.L.; resources, L.-n.L.; data curation, J.-s.K.; writing--original draft preparation, L.-n.L.; writing--review and editing, J.-s.K.; visualization, L.-n.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 work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2019R1G1A1098184) and part of the project titled 'Development of cloud-based next-generation VTS Integration platform, funded by the Korea Coast Guard (KIMSI-20210591).  
Not applicable.