## 5 Discussion  
This study collected AIS data from container ships arriving at and departing from Busan New Port in the Korea and analyzed the pattern of their trajectories using the DBSCAN algorithm. The methodology is novel in its analysis of the specifics of ship maneuvering in a port, which is lacking in the literature, in contrast to navigational route pattern analyses between pilot stations, which has been extensively studied previously. Additionally, it is meaningful that the ship trajectory was quantitatively analyzed using a machine learning algorithm based on the AIS data of ships that were in fact safely berthed. In particular, if this study is combined with existing berthing- and unberthing-related studies, it would form part of a basic evaluation of MASSs' in-port maneuvering and auto-berthing. For instance, these results can be combined with existing studies relating to range predictions of berthing velocity and the analysis of the patterns of berthing maneuvering by pilots [15; 16; 20]. All of these studies were conducted on the basis of measured data, and machine learning and artificial intelligence technologies were used. Therefore, this will constitute a core study relating to navigational ship operation between berths by MASSs. In addition, a major advantage of implementing a MASS is that it can prevent human error. Including the crane collision accident of a container ship at Busan New Port in April of 2020, which was the inspiration for this study, accidents in ports cause damage not only \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scaled Time**} & \multicolumn{5}{c}{**Speed Over Ground**} & \multicolumn{5}{c}{**Course Over Ground**} \\ \cline{2-10}  & **Mean** & **Std.** & **Min** & **Median** & **Max** & **Mean** & **Std.** & **Min** & **Median** & **Max** \\ \hline
0 & 11.78 & 1.36 & 8.40 & 11.80 & 14.60 & 161.23 & 3.43 & 152.50 & 161.90 & 167.20 \\
1 & 11.67 & 1.59 & 8.60 & 11.50 & 14.50 & 160.38 & 4.49 & 149.90 & 161.80 & 165.80 \\
2 & 11.84 & 1.95 & 8.90 & 11.50 & 16.20 & 159.29 & 4.79 & 143.30 & 160.50 & 166.50 \\
3 & 12.39 & 2.19 & 9.10 & 12.00 & 17.50 & 159.11 & 4.97 & 147.00 & 159.70 & 166.90 \\
4 & 12.14 & 2.28 & 9.10 & 11.40 & 18.60 & 156.00 & 6.10 & 138.80 & 157.10 & 166.40 \\
5 & 12.40 & 2.40 & 8.30 & 11.90 & 18.70 & 154.69 & 6.09 & 138.60 & 153.75 & 166.20 \\
6 & 12.34 & 2.29 & 8.20 & 11.80 & 18.90 & 151.99 & 5.61 & 138.20 & 151.80 & 163.60 \\
7 & 12.29 & 2.47 & 8.40 & 11.70 & 19.10 & 149.86 & 6.34 & 134.90 & 150.80 & 163.80 \\
8 & 12.73 & 2.62 & 6.70 & 12.00 & 19.40 & 150.80 & 3.13 & 138.40 & 151.30 & 163.90 \\
9 & 12.39 & 2.53 & 8.80 & 11.70 & 17.40 & 148.71 & 8.40 & 112.50 & 150.35 & 165.60 \\
10 & 12.84 & 2.60 & 5.90 & 12.00 & 19.70 & 149.93 & 9.76 & 88.70 & 151.50 & 164.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Descriptive statistics for the departure phase of the scaled time series. Figure 37: Boxplot time series analysis of the departure phase: (**a**) SOC; (**b**) COG; * the time series analysis for ship trajec Table 11. stages, from 0 to 10, by multiplying the result scaled in Equation (1) by 10. In addition, the boxplot was used to visually express the descriptive statistics for 11 stages. to ship hulls but also to human life and the environment, as well as significant economic damage, and so disaster prevention is vital. This is achievable as most of these kinds of incidents are a result of human error [6]. The short-term practical aspects of the analytical results of this paper are as follows. The result of analyzing patterns in detail by dividing them into arrival and departure phases can inform ship maneuvering guidelines for major ports in the Korea. In the case of Busan New Port, which is the target port in this study, the time series-based guideline is shown in a scatter graph in Figures 38 and 39 and is divided into arrival and departure segments, respectively. The \(x\)-axis is a function of multiplying the scaled time data for each ship by 100.