# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 4 Experiment
### Analysis and Comparison of Results  
Table 1 shows that our CGLGCN performed best for both datasets in terms of all metrics. It can be seen that the prediction results of conventional time series analysis methods were usually not ideal. This proves that the capabilities of such methods for model nonlinear and complex flow data are suboptimal. Generally, methods based on deep learning achieved better prediction results than conventional time series analysis methods. Our model considered temporal correlation and spatial correlation simultaneously, and so achieved better prediction results than LSTM and GRU models that considered only temporal correlation. Our model thus further reduced prediction errors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**PeMS04 (5/15/30/45 min)**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**PeMS08 (5/15/30/45 min)**} \\ \cline{2-2} \cline{4-6}  & **MAE** & **RMSE** & & **MAE** & **RMSE** \\ \hline HA & 35.12/35.75/36.89/37.25 & 41.25/44.97/52.87/54.89 & HA & 26.98/27.77/28.45/29.12 & 32.87/35.31/42.08/47.29 \\ \hline ARIMA & 30.06/30.56/31.95/32.63 & 33.12/43.98/59.87/65.73 & ARIMA & 21.43/22.02/29.89/23.87 & 22.47/29.15/39.21/48.34 \\ \hline VAR & 30.58/31.72/32.69/33.52 & 46.29/49.92/52.87/54.89 & VAR & 19.19/19.56/20.35/21.12 & 22.12/26.93/31.45/35.91 \\ \hline LSTM & 27.57/28.03/28.65/29.15 & 34.05/37.72/44.89/47.01 & LSTM & 21.41/21.85/22.45/23.21 & 21.32/27.73/36.78/42.51 \\ \hline GRU & 26.41/27.08/28.23/29.12 & 34.12/37.59/45.12/46.85 & GRU & 19.52/19.98/20.82/22.46 & 21.39/26.69/37.83/43.28 \\ \hline CGGCN & 23.15/24.08/25.23/26.12 & 32.12/33.49/35.15/37.90 & CGGCN & 17.52/17.98/18.84/20.46 & 21.15/22.49/24.15/25.85 \\ \hline CGLGCN & 22.12/22.94/23.51/24.24 & 31.23/32.45/34.05/35.91 & CGLGCN & 16.41/170.1/17.74/18.84 & 20.28/21.87/23.29/24.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different methods applied to PeMS04 and PeMS08. Figures 5 and 6 show the varying performance of different methods when the prediction was carried out for a longer period. In general, the models' prediction precision became lower when the prediction spanned a longer period. The models which only considered the temporal correlation, including ARIMA, LSTM, and GRU, achieved almost the same quality of results as our model in the 5-min short-term traffic flow prediction, but their performance declined sharply when the prediction time became longer. The VAR model considering both temporal and spatial correlations performed stably. However, it showed significant differences for different sample scales, and its performance badly declined with the expansion of the road network scale. Although the VAR model considers temporal and spatial correlations, it is not applicable to traffic flow prediction for large-scale road networks, and real-world road networks are usually huge. Our CGLGCN model performed well in the test. It was shown to be competent for the prediction task no matter whether the road network was large or small, and always delivered adequately stable performance even when the prediction spanned a longer period. As shown in Figures 7 and 8, we performed ablation experiments on CGLGCN on pems04 and pems08 datasets, respectively. The model that does not use our low-pass filtering constraints is labelled CGGCN. Experiments demonstrated that our model using a low-pass filter constraint had better performance, which proves the feasibility of our low-pass filter constraint. Figure 5: Performance comparison of different models over time for the PeMS04 dataset. Figure 6: Performance comparison of different models over time for the PeMS08 dataset. Our model abandons the LSTM and GRU models that use multiple parameters and involve considerable time consumption, and uses gated linear units to capture the features of time dimension, which greatly reduces the training time.