## 4 Prediction Algorithm of Maritime Traffic Condition
### Experimental Result  
Based on model training and validation according to the scenario configurations, the valid loss was determined to be 0.2717 in scenario (a) using only congestion. However, in scenario (b) where the fluctuation range, fluctuation rate, and probability of varying within the fluctuation range were examined, the model performance improved to 0.2182.  
Scenario (c) considered congestion and the average value of congestion on days 3, 6, 12, and 24 and scenario (d) considered the maximum value on days 3, 6, 12, and 24; they exhibited valid losses of 0.1490 and 0.1164, respectively. These values were higher than that of the previous two scenarios. Particularly, in the case of scenario (d), relatively high congestion was well predicted. Scenario (e) utilized congestion, the number of ships, and traffic volume, and had a valid loss of 0.2047. This suggested that the performance  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Batch Size & 24 \\ \hline Dropout Rate & 0.6 \\ \hline Epoch & 500 \\ \hline Learning Rate & 0.001 \\ \hline Time Step & 24 \\ \hline Optimizer & Adam \\ \hline Weight initializer & Glorot\_uniform \\ \hline Gradient clipping & Clip\_by\_value \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters for training.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Input**} & \multirow{2}{*}{**Output**} \\ \cline{2-2} \cline{4-4}  & **Trend** & & **Prediction** \\ \hline (a) & Congestion & - \\ \hline (b) & Congestion & - \\  & Variance width/rate/Prob. & \\ \hline (c) & Congestion & - \\  & 3, 6, 12, 24 avg & \\ \hline (d) & Congestion & - \\  & 3, 6, 12, 24 max & \\ \hline (e) & Congestion & Num of ship \\  & & Traffic Vol \\ \hline (f) & Congestion & \\  & Variance width/rate/Prob. & Num of ship \\  & 3, 6, 12, 24 avg & Traffic Vol \\  & 3, 6, 12, 24 max & \\ \hline (g) & - & Congestion (B gate) \\  & Congestion (C gate) \\ \hline (h) & Congestion & Number of ships \\  & Variance width/rate/Prob. & Traffic Volume \\  & 3, 6, 12, 24 avg & Congestion (B gate) \\  & 3, 6, 12, 24 max & Congestion (C gate) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of training and experimental scenarios.  
of the model improved, similar to scenario (b). When all the features were used except for the congestion of the other gate lines in scenario (f), the valid loss was 0.0842, which exhibited high model performance. When using the congestion of the other gate lines in scenario (g), the valid loss was 0.1520, which demonstrated that the performance of the model was further improved compared to scenarios (b) and (e). When all features were used in scenario (h), the valid loss was 0.0835, which was the highest model performance.  
Figure 7 and Table 4 depict the model training results for each scenario, where sky blue represents the validated data and orange indicates the predicted data.  
Figure 7: Model training results under each scenario: (**a**) Scenario with input data as Congestion; (**b**) Scenario with input data as Congestion, Variance width/rate/Prob.; (**c**) Scenario with input data as Congestion, 3, 6, 12, 24 avg.; (**d**) Scenario with input data as Congestion, 3, 6, 12, 24 max.; (**e**) Scenario with input data as Congestion, Num of ship, Traffic Vol.; (**f**) Scenario with input data as Congestion, Variance width/rate/Prob., 3, 6, 12, 24 avg., 3, 6, 12, 24 max., Num of ship, Traffic Vol.; (**g**) Scenario with input data as Congestion(B Gate), Congestion(C Gate); (**h**) Scenario with input data as Congestion, Variance width/rate/Prob., 3, 6, 12, 24 avg., 3, 6, 12, 24 max., Num of ship, Traffic Vol., Congestion(B Gate), Congestion(C Gate). The sky blue line represents actual data and orange line represents predicted data.