## 3 Results
Figure 7: Data extraction by target area: (**a**) data collected, (**b**) target area, and (**c**) data classified by target area. Figure 10 and Table A1 show the results of the DBSCAN simulation performed to select appropriate DBSCAN parameters. The selected cluster IDs are 17, and the calculated CPA and TCPA values from each cluster are used to set the fuzzy membership function. A fuzzy membership function was formed for each cluster ID, as shown in Table 4. In each cluster, D of CPA stands for danger, implying CPA is zero; T for threat, implying the minimum value; C for caution, implying the median value; and A for attention, implying the maximum value. A variable value was applied according to the clustering area. Further, D of TCPA stands for danger, implying TCPA zero; T for threat, implying 120 s; C for caution, implying 240 s; and A for attention, implying 360 s. Meanwhile, TCPA decision criteria were applied via the conversion of the relative distance of the vessel into time according to the COLREGs rules and a guide to the collision avoidance rules of the IMO. Figure 11 shows the simulation results of Pindex extraction. The visualization result of the small map in Figure 11b was posted after extracting all priorities for the priority index results of all dangerous vessels. In the target area, the priority index results were found as follows: there was 1 case of near collision with priority index 0.85 or higher, 10 cases of high danger with index 0.8 or higher, and 93 cases of caution with index 0.7. A verification simulation was performed with 18 VTSOs to verify whether the proposed VTS priority index can aid the VTSOs in decision making. The group was classified into two VTSO groups with nine operators each that searched for vessel pairs with and without the Pindex provided, respectively. Each group consisted of six operators with five years of experience and three operators with between five and ten years of experience. Subsequently, a method of checking the real-time navigation data of the target sea area, as shown in Figure 11a, and then searching for the vessel pair that requires priority control within the time limit, was applied. The total number of sailing vessels was 61, and the number of cases involving encountering vessels was 29. Thus, verification was performed by measuring the number and time of searches for a vessel pair under control within 30 s,  
Figure 9: Comparison of risk perceptions of ship traffic situations in the identical conditions: (**a**) own ship’s point of view, and (**b**) VTSO’s point of view. targeting 10 cases with a Pindex of 0.8 or higher. In the VTSO group using Pindex, all 10 cases were searched within 30 s, requiring 10.4 s on average. The successful search for a vessel pair within the time limit ensures timely maritime traffic management activities. Figure 10: DBSCAN simulation results: (**a**) Eps 0.010/MinPts 5, (**b**) Eps 0.015/MinPts 5, (**c**) Eps 0.020/MinPts 5, (**d**) Eps 0.010/MinPts 10, (**e**) Eps 0.015/MinPts 10, and (**f**) Eps 0.020/MinPts 5. To verify the main results of the study, we compared the VTS operation on traffic management between the method used in the existing VTS system and the method of performing the task by presenting Pindex to the VTSO. The key strength of Pindex is that the priority of all ships in the entire target area can be considered simultaneously, rather than making a decision by listing the CPA and TCPA for each pair of ships. This implies that the VTSO responds to dangerous situations by recognizing the situations in advance according to their importance. In previous studies, collision risk was presented by classifying the levels of CPA and TCPA; however, the index was extracted using fixed CPA and TCPA level values without considering regional factors [33] or using a fixed ship safety domain [31]. In contrast, the algorithm in this study was constructed based on the