## 4 Experiments
### Experimental Results
#### 4.5.2 Model Structure Comparison  
In this section, we mainly discuss the structural differences between STGCN [13], ST-MetaNet [37], Graph WaveNet [14], and our proposed model GSTGCN. STGCN is a deep learning framework with complete convolutional structures. It contains multiple 1D casual convolutions followed by a gated linear unit (GLU) for capturing temporal correlations and employs K-order Chebyshev graph convolution on traffic data to extract spatial dependencies. The architecture only captures simple nonlinear temporal correlations and localized spatial dependencies of traffic data. We can observe that STGCN performs poorly compared to the other models in Figure 9a, especially in the case of long-term prediction. \begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**15 min**} & \multicolumn{4}{c}{**30 min**} & \multicolumn{4}{c}{**1 h**} \\ \cline{3-11}  & & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{9}{*}{PeMSD7} & ARIMA & 5.68 & 9.38 & 12.36\% & 6.29 & 9.98 & 13.67\% & 7.23 & 11.02 & 14.52\% \\  & SVR & 3.67 & 5.52 & 7.64\% & 4.28 & 8.02 & 9.19\% & 5.14 & 9.67 & 11.27\% \\  & SAE & 3.44 & 5.25 & 7.24\% & 4.01 & 7.89 & 8.89\% & 4.92 & 9.25 & 10.43\% \\  & SBU-LSTM & 3.52 & 5.05 & 7.01\% & 3.95 & 7.42 & 8.52\% & 4.78 & 8.93 & 9.87\% \\  & DCRNN & 2.46 & 4.52 & 5.50\% & 3.32 & 6.51 & 7.94\% & 4.37 & 8.23 & 8.78\% \\  & STGCN & 2.34 & 4.40 & 5.36\% & 3.28 & 6.34 & 7.80\% & 4.16 & 7.86 & 8.80\% \\  & ST-MetaNet & 2.05 & 3.95 & 4.52\% & 2.81 & 5.66 & 6.23\% & 3.57 & 7.10 & 8.01\% \\  & Graph WaveNet & 2.04 & 3.92 & 4.72\% & 2.74 & 5.54 & 6.83\% & 3.36 & 6.74 & 8.74\% \\  & GSTGCN & **1.20** & **2.16** & **2.66\%** & **1.81** & **3.03** & **4.58\%** & **3.05** & **4.63** & 8.63\% \\ \hline \multirow{9}{*}{PeMSD4} & ARIMA & 5.04 & 7.45 & 10.86\% & 6.06 & 8.09 & 11.05\% & 6.98 & 9.78 & 12.32\% \\  & SVR & 3.08 & 4.62 & 6.54\% & 4.09 & 6.72 & 7.78\% & 5.67 & 8.13 & 10.96\% \\  & SAE & 2.90 & 4.44 & 6.25\% & 3.98 & 6.48 & 7.52\% & 5.28 & 7.66 & 8.34\% \\  & SBU-LSTM & 2.10 & 4.01 & 6.09\% & 3.66 & 6.26 & 7.05\% & 4.33 & 6.85 & 7.88\% \\  & DCRNN & 2.15 & 3.45 & 4.38\% & 2.90 & 5.15 & 5.74\% & 3.49 & 5.78 & 6.17\% \\  & STGCN & 2.05 & 3.30 & 4.27\% & 2.85 & 4.98 & 5.65\% & 3.24 & 5.65 & 5.98\% \\  & ST-MetaNet & 1.21 & 2.73 & 4.01\% & 1.79 & 4.17 & 3.92\% & 2.31 & 5.12 & 4.83\% \\  & Graph WaveNet & 1.31 & 2.76 & 2.75\% & 1.66 & 3.75 & 3.69\% & 2.00 & 4.61 & **4.73\%** \\  & GSTGCN & **0.73** & **1.65** & **1.78\%** & **1.31** & **3.17** & **2.94\%** & **1.89** & **3.43** & 4.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different approaches for traffic prediction on PeMSD7 and PeMSD4 datasets. The best results are marked in bold. Figure 8: Speed prediction in the morning peak hours and weekends of the dataset PeMSD7. ST-MetaNet employs a sequence-to-sequence architecture. It introduces meta-learning to spatiotemporal modeling. The model first utilizes the points of interests (POIs) and density of road network around the detector to construct node attributes and then constructs the graph's edge attributes using k-nearest neighbor (KNN) algorithm. In the model, a meta graph attention network (GAT) is used to capture diverse spatial correlations, and a meta recurrent neural network (RNN) is employed to consider diverse temporal correlations. Compared with the ordinary 1D casual convolution, RNN has the advantage for time series modeling, as it can remember the previous input sequence using its inner memory structure. Besides, ST-MetaNet takes meta-learning knowledge into account. Thus, it is superior to STGCN in Table 3 and Figure 9a. Graph WaveNet is a graph neural network architecture for spatial-temporal graph modeling. In the spatial dimension, Graph WaveNet introduces an adaptive adjacency matrix to capture spatial correlation based on diffusion convolution.