## 5 Experiments
### Experiment Results  
#### 5.4.1 Model Prediction Effects Comparison  
The comparison of the prediction effects of various models is shown in Table 3. The LGTCN has achieved in ten out of the twelve indicators and has superior prediction performance. On all four datasets, our experimental results performed consistently well. Our proposed model, LGTCN, consistently outperformed the other baseline models on its MAE and RMSE. In the MAPE, our model took the lead on two datasets, but slightly fails on two datasets. One of the reasons for this is the presence of 0 and tiny values in the data, which leads to the nonapplicability of the MAPE. Another reason is that the loss function of the model is the MAE, and the model is optimized in the direction of minimum MAE value. Nonetheless, we argue that our model achieves an important improvement in prediction performance compared to the baseline models.  
The FC-LSTM can only use temporal correlation and cannot use spatial correlation, so the prediction effect is the worst. The DCRNN, STGCN, and ASTGCN(r) can use different modules to utilize the temporal and spatial correlation of data, so the prediction accuracy has improved. The STSGCN can simultaneously extract temporal and spatial information, and the STFGNN model can take into account long-range spatial correlations, so it exhibits excellent predictive performance.  
Our model can extract global spatial correlations with a small computational cost and then use a multichannel temporal convolutional network to achieve refined extraction of temporal correlations, and the comprehensive prediction performance is the best.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Datasets** & **Metrics** & **FC-LSTM [60]** & **DCRNN [19]** & **STEGCN [8]** & **ASTEGCN(H) [32]** & **STEGCN [97]** & **STFGNN [19]** & **LGTCN** \\ \hline \multirow{3}{*}{PEMS03} & MAE & 21.33 \(\pm\) 0.24 & 18.18 \(\pm\) 0.15 & 17.49 \(\pm\) 0.46 & 17.69 \(\pm\) 1.43 & 17.48 \(\pm\) 0.15 & 16.77 \(\pm\) 0.09 & **15.21 \(\pm\) 0.04** \\  & MAE(\(\%\)) & 23.33 \(\pm\) 0.23 & 18.91 \(\pm\) 0.82 & 17.15 \(\pm\) 0.45 & 19.40 \(\pm\) 2.24 & 16.78 \(\pm\) 0.20 & 16.30 \(\pm\) 0.09 & **14.79 \(\pm\) 0.20** \\  & RMSE & 35.11 \(\pm\) 0.50 & 30.31 \(\pm\) 0.25 & 30.12 \(\pm\) 0.70 & 29.66 \(\pm\) 1.68 & 29.21 \(\pm\) 0.56 & 28.34 \(\pm\) 0.46 & **24.11 \(\pm\) 0.07** \\ \hline \multirow{3}{*}{PEMS04} & MAE & 27.14 \(\pm\) 0.20 & 24.70 \(\pm\) 0.22 & 22.07 \(\pm\) 0.64 & 22.93 \(\pm\) 1.29 & 21.19 \(\pm\) 0.10 & 19.83 \(\pm\) 0.06 & **19.58 \(\pm\) 0.09** \\  & MAE(\(\%\)) & 18.20 \(\pm\) 0.40 & 17.12 \(\pm\) 0.37 & 14.99 \(\pm\) 0.21 & 16.56 \(\pm\) 1.36 & 13.90 \(\pm\) 0.05 & **13.02 \(\pm\) 0.05** & 15.92 \(\pm\) 0.31 \\  & RMSE & 41.59 \(\pm\) 0.21 & 38.12 \(\pm\) 0.26 & 35.55 \(\pm\) 0.75 & 35.22 \(\pm\) 1.90 & 33.65 \(\pm\) 0.20 & 31.88 \(\pm\) 0.14 & **31.37 \(\pm\) 0.14** \\ \hline \multirow{3}{*}{PEMS07} & MAE & 29.98 \(\pm\) 0.42 & 25.30 \(\pm\) 0.52 & 25.38 \(\pm\) 0.49 & 28.08 \(\pm\) 2.34 & 24.26 \(\pm\) 0.14 & 22.07 \(\pm\) 0.11 & **22.03 \(\pm\) 0.13** \\  & MAE(\(\%\)) & 13.20 \(\pm\) 0.53 & 11.66 \(\pm\) 0.33 & 11.08 \(\pm\) 1.03 & 13.92 \(\pm\) 1.65 & 10.21 \(\pm\) 1.05 & **9.21 \(\pm\) 0.07** & 9.96 \(\pm\) 0.14 \\  & RMSE & 45.94 \(\pm\) 0.57 & 38.58 \(\pm\) 0.70 & 38.78 \(\pm\) 0.58 & 42.57 \(\pm\) 3.31 & 39.03 \(\pm\) 0.27 & 35.80 \(\pm\) 0.18 & **34.53 \(\pm\) 0.16** \\ \hline \multirow{3}{*}{PEMS08} & MAE & 22.20 \(\pm\) 0.18 & 17.86 \(\pm\) 0.03 & 18.02 \(\pm\) 0.14 & 18.60 \(\pm\) 0.40 & 17.13 \(\pm\) 0.09 & 16.64 \(\pm\) 0.09 & **16.20 \(\pm\) 0.08** \\  & MAE(\(\%\)) & 14.20 \(\pm\) 0.59 & 11.45 \(\pm\) 0.03 & 11.40 \(\pm\) 0.10 & 13.08 \(\pm\) 1.00 & 19.96 \(\pm\) 0.07 & 10.02 \(\pm\) 0.06 & **10.56 \(\pm\) 0.20** \\  & RMSE & 34.06 \(\pm\) 0.32 & 27.83 \(\pm\) 0.05 & 27.83 \(\pm\) 0.20 & 28.16 \(\pm\) 0.48 & 26.80 \(\pm\) 0.18 & 26.22 \(\pm\) 0.15 & **25.22 \(\pm\) 0.12** \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different models in traffic flow dataset prediction.