## 6 Conclusions  
In this study, in an effort to propose new ship maneuvering guidelines for the port, ship trajectories were divided into phases using the DBSCAN algorithm, and these patterns were analyzed. The results of this analysis can be used to conduct basic research on the future development of MASS ship maneuvering guidelines based on AI technology. Therefore, the findings of the study are summarized as follows:  
Figure 39: Scatter graph of departing ship trajectories for maneuvering guidelines. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Guideline Items**} & \multicolumn{3}{c}{**R-Squared Values of the Fitting Line**} \\ \cline{2-3}  & **Arrival** & **Departure** \\ \hline Speed over ground & 0.7788 & 0.8414 \\ Course over ground & 0.4564 & 0.4186 \\ Ship’s position (latitude) & 0.9756 & 0.9800 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: R-square analysis result of the fitting line. * First, in order to propose a ship port maneuvering guideline, the target port was set as Busan New Port and AIS data on the arrival and departure of ships were collected. The collection period was from January to April, 2020, and the targets were container ships with a gross tonnage of 100,000 tons or more. The collected data were a case in which 42 ships were targets for both arrival and departure, and a total of 50 berthings were completed. * Secondly, the collected data were pre-processed to render it suitable for analysis. In addition to organizing the data within the scope of the analysis by means of data cleaning, the unit of \"date and time\" was scaled to suit the time series analysis via data scaling. Then, the total dataset was separated into a range corresponding to arrivals and departures. * Thirdly, the dataset separated by arrivals and departures was applied to the DBSCAN algorithm. The most important parameters of the DBSCAN algorithm, epsilon and minimum samples, were set to 0.2 and 50, respectively. As a result, arrival was classified into seven stages, including leg, turning section, and berth clustering, and the departure was divided into six, including unberthing, leg, and turning section clusters. These were in turn divided into four phases again, and the pattern was analyzed. * Finally, the pattern of the ship trajectories was divided into four phases for both arrivals and departures and analyzed in detail. Analysis items included speed over ground, course over ground, and ship position. In order to analyze the patterns of the ships' trajectories, a frequency count analyis for each item of each phase was performed and a boxplot according to the time series was proposed. In particular, during the berthing and unberthing phases, the cases of passing Todo in Busan New Port on either the left or the right side were separately analyzed. * By synthesizing the results, we derived the degree of change in terms of speed over ground, course over ground, and ship position according to the stated time series. This can be utilized as a ship maneuvering guideline for the port. In addition, if trajectory data on ships safely berthet at each port are collected and analyzed, it will be possible to contribute to the development of MASS ship maneuvering technology in ports. Conceptualization, H.-T.L. and I-S.C.; data curation, H.-T.L. ; formal analysis, H.-T.L. ; investigation, H.-T.L. ; methodology, H.-T.L. ; software, H.-T.L. and J.-S.L. ; supervision, H.Y. and I-S.C.; validation, H.-T.L., J.-S.L., H.Y. and I-S.C.; visualization, H.-T.L. and J.-S.L. ; writing--original draft preparation, H.-T.L. ; writing--review and editing, H.-T.L. and I-S.C. All authors have read and agreed to the published version of the manuscript. The APC was funded by the Development of Autonomous Ship Technology (20200615, Development of Shore Remote Control System of MASS). Not applicable. Restrictions apply to the availability of these data. Data were obtained from the Busan Port Authority.