# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 4 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{Rotterdam} & \multicolumn{3}{c}{Boston} & \multicolumn{3}{c}{Antwerp} & \multicolumn{3}{c}{Hong Kong} \\ \cline{2-13}  & MAE & MAPE & RMSE & MAE & MAPE & RMSE & MAE & MAPE & RMSE & MAE & MAPE & RMSE \\ \hline RNN & 4.5813 & 0.6493 & 23.1354 & 3.2588 & 0.6722 & 6.4718 & 2.5007 & 0.6392 & 4.9709 & 16.3516 & 1.0782 & 33.7049 \\ Auto & 4.084 & 1.1835 & 11.4571 & 3.2034 & 0.625 & 6.3242 & 2.9329 & 0.6687 & 4.4949 & 18.3066 & 1.7544 & 32.9607 \\ Encoder & 4.8228 & 0.6932 & 2.4695 & 3.3392 & 0.6943 & 6.7335 & 2.4829 & 0.6209 & 5.0688 & 16.6819 & 0.9056 & 33.5273 \\ WaveNet & 2.5465 & **0.4315** & 9.4525 & 2.3622 & 0.4396 & 4.1007 & 1.8745 & 0.5385 & 3.2327 & 8.6431 & **0.432** & 18.5065 \\ Transformer & 2.5101 & 0.4891 & **8.401** & 2.1902 & 0.473 & 3.79 & 2.0819 & 0.5873 & 3.8074 & 8.5305 & 0.4564 & 17.6556 \\ \hline STGCN & 3.2307 & 0.7186 & 9.5492 & 2.2647 & 0.498 & 3.9539 & 2.1825 & 0.5937 & 3.6725 & 13.1973 & 0.9862 & 27.4905 \\ AGCRN & 3.2396 & 0.5869 & 13.6822 & 2.5943 & 0.5111 & 4.7692 & 2.0179 & 0.5307 & 3.6471 & 9.5976 & 0.5001 & 21.5886 \\ DCRNN & 2.8718 & 0.616 & 9.8986 & 2.1044 & 0.4642 & 3.8234 & 1.7907 & 0.4090 & 3.191 & 8.9693 & 0.5549 & 18.9956 \\ STIN & 3.8664 & 0.6449 & 12.9455 & 2.2696 & 0.4827 & 4.0493 & 2.3098 & 0.5858 & 4.1153 & 16.9211 & 0.9443 & 33.8544 \\ STIGAT & 2.762 & 0.5231 & 8.6717 & 2.2309 & 0.4816 & 3.8571 & 1.7959 & 0.5038 & 3.0626 & 9.0416 & 0.6927 & 17.7911 \\ GWNET & 2.4831 & 0.4498 & 9.0111 & 2.1861 & 0.4421 & 4.0143 & 1.9133 & 0.5216 & 3.4256 & 8.5496 & 0.4489 & 17.6672 \\ \hline GAGW & **2.3964** & 0.4485 & 8.514 & **2.0983** & **0.434** & **3.7663** & **1.7669** & **0.4719** & **3.0236** & **8.216** & 0.4436 & **17.2679** \\ \hline \multicolumn{13}{c}{Singapore} & \multicolumn{3}{c}{Inchen} \\ \cline{2-13}  & MAE & MAPE & RMSE & MAE & MAPE & RMSE & MAE & MAPE & RMSE & MAE & MAPE & RMSE \\ \hline RNN & 18.1033 & 18.698 & 36.8206 & 10.1521 & 0.8326 & 31.2282 & 18.4991 & 1.3414 & 37.6816 & \\ Auto & 17.5359 & 1.8645 & 35.2272 & 8.5357 & 0.7448 & 24.0477 & 19.6817 & 1.8012 & 37.5785 & \\ Encoder & 14.8196 & 1.1846 & 29.9094 & 10.1913 & 0.8657 & 31.0003 & 19.0404 & 1.3452 & 40.7782 & \\ WaveNet & 7.4423 & 0.4437 & 15.98 & 5.5169 & 0.5057 & 14.8807 & 8.7696 & **0.4229** & 19.9683 & \\ Transformer & 7.5331 & 0.4567 & 15.9231 & **4.3022** & 0.4288 & **11.5323** & **0.3554** & 0.4245 & **18.5827** & \\ \hline STGCN & 11.8722 & 0.6802 & 25.1572 & 7.9983 & 1.8568 & 16.0688 & 11.8275 & 0.783 & 26.6319 & \\ AGCRN & 8.5064 & 0.6398 & 17.7152 & 4.7981 & 0.5475 & 12.9965 & 9.6238 & 0.5119 & 20.6766 & \\ DCRNN & 7.6933 & 0.6046 & 16.1944 & 4.8215 & 0.5592 & 12.6221 & 8.9704 & 0.5052 & 19.4997 & \\ STIN & 14.6455 & 1.1332 & 30.7543 & 9.279 & 0.8025 & 25.8419 & 19.6088 & 1.2163 & 40.3185 & \\ STIGAT & 7.6263 & 0.4518 & 15.718 & 4.9113 &In the comparison of spatiotemporal graph models, the overall predictive performance of the STGCN model is relatively poor, which can be attributed to its use of a coarse-grained temporal processing strategy. The relatively simple gate convolution structure of STGCN is unable to fully capture the complex traffic flow variations in maritime scenarios, resulting in its performance being inferior to that of more sophisticated temporal prediction models such as Transformer. In this experiment, the performance of the STTN model, which integrates self-attention mechanisms in both the temporal and spatial layers, did not meet our expectations. Despite adjusting certain hyperparameters, we were unable to obtain satisfactory experimental results. We speculate that the underlying reason for this lies in the spatial feature capturing strategy based on self-attention mechanisms. In the maritime context of this study, this strategy fails to effectively represent the data-driven, cross-spatial graph correlation structures, thereby leading to a decline in model performance. The STMGAT, GWNET, and the proposed model in this paper all employ a time-convolution-based temporal layer processing strategy.