# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 4 Results
### Experimental Results
The experimental results demonstrate that gate-based processing structures centered around causal convolutions can better capture the long-term variations in traffic flow data, enabling the fine-grained analysis of temporal features and obtaining more accurate prediction results. The training curves of the top-performing three spatiotemporal graph models in the traffic flow scenario centered around the Rotterdam Port cluster are shown in Figure 8. The proposed model in this paper achieves more accurate predictive performance by utilizing a multi-head attention mechanism similar to STTN and STMGAT in the graph convolutional layer. However, this improvement in accuracy comes at the expense of sacrificing some training speed, resulting in a convergence speed that is not state-of-the-art. As illustrated in Figure 9, our model's traffic flow predictions exhibit better adherence to the actual flow variation patterns compared to other baseline models and demonstrate good fitting capability for significant flow fluctuation curves. Figure 8: Comparison of MAE training curves for top three models. To further investigate the stability of the model in the small-sample maritime test scenario, we extracted the traffic flow data from June to September as a specific monthly dataset for independent testing (Table 3). These four months are characterized by substantial fluctuations in port traffic, without a discernible pattern in the context of international shipping. Moreover, the navigation relationships among ports during this period are more intricate, placing higher demands on the predictive capabilities of the models. In this challenging scenario, the GAGW model stands out among numerous spatiotemporal prediction models and exhibits exceptional adaptability to the limited coverage of the specific monthly dataset. This further confirms the effectiveness of the new architecture. To validate the issue of the unequal treatment of spatial and temporal features, we conducted ablation experiments by modifying some existing spatiotemporal graph model architectures to incorporate the proposed gate-based mechanism for spatiotemporal feature fusion. The experimental results, as shown in Table 4, indicate that the spatiotemporal graph models with the inclusion of the feature fusion structure generally outperform the models with the original architectures in the port traffic prediction scenario of this paper. Among them, since the baseline models AGCRN and DCRNN both belong to iterative architectures based on RNN, it is difficult to incorporate the fusion module. Therefore, we introduced the ASTGCN [39] model, which simultaneously uses attention mechanisms and temporal convolution strategies, as the new baseline model. To validate the relationship weights between time and spatial features, we conducted ablation experiments on the temporal and spatial layers of the GAGW model. In Table 5, \"\(w/o\) T\" represents the removal of all temporal hidden layers in the model, while \"\(w/o\) S\" indicates the removal of all graph structure processing methods. The experimental results demonstrate that ablating the temporal layer has a more severe impact on spatiotemporal graph models, leading to a significant decrease in predictive performance. In comparison, the performance decline is relatively limited when the spatial layer is ablated, further confirming the inequality between time and spatial features. Figure 9: Comparison of GAGW and other models with ground truth inbound flow. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MAE**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**R\({}^{2}\)**} \\ \hline _w/o_ **T** & 12.5244 & 21.3558 & 0.9323 \\ _w/o_ **S** & 10.2882 & 20.0338 & 0.9406 \\
**GAGW** & **10.1879** & **19.9007** & **0.9412** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of METR_LA.