## 5 Experiments
### Experimental Results and Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**15 min**} & \multicolumn{3}{c}{**30 min**} & \multicolumn{3}{c}{**60 min**} \\ \cline{3-11}  & & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{8}{*}{METR-LA} & ARIMA & 3.99 & 8.21 & 9.60\% & 5.15 & 10.45 & 12.70\% & 6.90 & 13.23 & 17.40\% \\  & FC-LSTM & 3.44 & 6.30 & 9.60\% & 3.77 & 7.23 & 10.90\% & 4.37 & 8.69 & 13.20\% \\  & STGCN & 2.88 & 5.74 & 7.62\% & 3.47 & 7.24 & 9.57\% & 4.59 & 9.40 & 12.70\% \\  & DCRNN & 2.77 & 5.38 & 7.30\% & 3.15 & 6.45 & 8.80\% & 3.60 & 7.59 & 10.50\% \\  & GW-Net & 2.69 & 5.15 & 6.90\% & 3.07 & 6.22 & 8.37\% & 3.53 & 7.37 & 10.01\% \\  & AGCRN & 2.87 & 5.58 & 7.70\% & 3.23 & 6.58 & 9.00\% & 3.62 & 7.51 & 10.38\% \\  & DSTAGCN & 2.74 & 5.24 & 7.12\% & 3.14 & 6.27 & 8.65\% & 3.59 & 7.33 & 10.26\% \\  & DSTGFCN & **2.53** & **4.97** & **6.52\%** & **2.88** & **6.03** & **7.79\%** & **3.28** & **7.13** & **9.59\%** \\ \hline \multirow{8}{*}{PEMS-BAY} & ARIMA & 1.62 & 3.30 & 3.50\% & 2.33 & 4.76 & 5.40\% & 3.38 & 6.50 & 8.30\% \\  & FC-LSTM & 2.05 & 4.19 & 4.80\% & 2.20 & 4.55 & 5.20\% & 2.37 & 4.96 & 5.70\% \\  & STGCN & 1.36 & 2.96 & 2.90\% & 1.81 & 4.27 & 4.17\% & 2.49 & 5.69 & 5.79\% \\  & DCRNN & 1.38 & 2.95 & 2.90\% & 1.74 & 3.97 & 3.90\% & 2.07 & 4.74 & 4.90\% \\  & GW-Net & 1.30 & 2.74 & 2.70\% & 1.63 & 3.70 & 3.7\% & 1.95 & 4.52 & 4.6\% \\  & AGCRN & 1.37 & 2.87 & 2.94\% & 1.69 & 3.85 & 3.87\% & 1.96 & 4.54 & 4.64\% \\  & DSTAGCN & 1.36 & 2.85 & 2.88\% & 1.70 & 3.84 & 3.83\% & 2.01 & 4.60 & 4.71\% \\  & DSTGFCN & **1.29** & **2.73** & **2.69\%** & **1.59** & **3.63** & **3.52\%** & **1.85** & **4.30** & **4.25\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of DSTGFCN and baseline models on traffic speed prediction tasks. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**PEMS03**} & \multicolumn{3}{c}{**PEMS04**} & \multicolumn{3}{c}{**PEMS07**} & \multicolumn{3}{c}{**PEMS08**} \\ \cline{2-11}  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline ARIMA & 35.41 & 47.59 & 33.78\% & 33.73 & 48.80 & 24.18\% & 38.17 & 59.27 & 19.46\% & 31.09 & 44.32 & 22.73\% \\ FC-LSTM & 21.33 & 35.11 & 23.33\% & 27.14 & 41.59 & 18.20\% & 29.98 & 45.84 & 13.20\% & 22.20 & 34.06 & 14.20\% \\ STGCN & 17.55 & 30.42 & 17.34\% & 21.16 & 34.89 & 13.83\% & 25.33 & 39.34 & 11.21\% & 17.50 & 27.09 & 11.29\% \\ DCRNN & 17.99 & 30.31 & 18.34\% & 21.22 & 33.44 & 14.17\% & 25.22 & 38.61 & 11.82\% & 16.82 & 26.36 & 10.92\% \\ GW-net & 19.12 & 32.77 & 18.89\% & 24.89 & 39.66 & 17.29\% & 26.39 & 41.50 & 11.97\% & 18.28 & 30.05 & 12.15\% \\ AGCRN & 15.98 & 28.25 & 15.23\% & 19.83 & 32.26 & 12.97\% & 22.37 & 36.55 & 9.12\% & 15.95 & 25.22 & 10.09\% \\ DSTAGCN & 15.31 & **25.30** & **14.91\%** & 19.48 & 30.98 & 12.93\% & 22.07 & 35.80 & 9.21\% & 15.83 & 24.70 & 10.03\% \\ STG-NCDE & 15.57 & 27.09 & 15.06\% & 19.21 & 31.09 & 12.76\% & 20.53 & 33.84 & 8.80\% & 15.45 & 24.81 & 9.92\% \\ DSTGFCN & **14.60** & 25.45 & 15.60\% & **18.53** & **30.51** & **12.37\%** & **19.70** & **32.94** & **8.39\%** & **15.25** & **24.56** & **9.88\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of DSTGFCN and baseline models for traffic flow prediction tasks. STG-NCDE were similar, but STG-NCDE performed better than AGCRN in short-term predictions. AGCRN exhibited good performance in long-term predictions on the PEMS08 dataset, where DSTGFCN slightly lagged behind AGCRN regarding MAPE. However, overall, DSTGFCN demonstrated lower errors across the entire time range, showcasing the superior performance of our model. Lastly, we found that the difficulty of prediction varied across different datasets.