## 3 Experiments
### Experimental Results and Discussion  
To verify the performance of the proposed EnGS-DGR model, eight classical baselines are employed, including the history average model (HA), the autoregressive integrated moving average model (ARIMA), the vector auto-regression model (VAR), the support vector regression model (SVR), the feedforward neural network (FNN), the recurrent neural network with fully connected LSTM hidden units (FC-LSTM), the spatial-temporal graph convolution network (STGCN), and the diffusion convolutional recurrent neural network (DCRNN). Considering the proposed EnGS-DGR model and the employed eight baselines with three forecasting intervals of 15 min, 30 min, and 60 min, the comparison curves of 24-h forecasting results and forecasting errors are shown in Figures 7 and 8, in which the  
Figure 6: Monitor distribution in PEMS-BAY and METR-LA dataset [23]. horizontal axis is separated by a 5 min time slice and the vertical axis represents the travel speeds of the forecasting results and ground truth. The most of forecasting errors between the proposed results of EnGS-DGR model and ground truth are constrained in the range of \([-2,2]\). It has been obviously found that the proposed EnGS-DGR model not only obtains good approximations of the ground truth but also tracks the velocity trend during different time frames. Considering the three evaluation performance metrics, the specific forecasting results are presented in Table 1 under the proposed EnGS-DGR model and the employed eight baselines. Taking 30 min forecasting on PEMS-BAY dataset as an example, for this comparative study, the detailed comparisons could be analyzed as follows:  
1. In comparison with RMSE values obtained from HA, ARIMA, SVR, the decrement rates of the proposed EnGS-DGR model are 61.5%, 19.7%, and 26.3%, respectively. Meanwhile, focused on MAE, the decrement rates of the proposed EnGS-DGR model are 41.3%, 27.5%, and 31.9% compared with those under HA, ARIMA, and SVR. The above comparison results show that the proposed EnGS-DGR model could be answerable for complex nonlinear traffic data series than employed three baselines effectively. 2. Compared with two GCN-based baselines of STGCN and DCRNN, the RMSE values under the EnGS-DGR model are reduced about 10.5% and 3.8%. In comparison with MAE values, the decrement rates of the proposed EnGS-DGR model are 6.6% and 2.9% compared with GCN and DCRNN, respectively. It can be seen that the proposed EnGS-DGR model performs better in forecasting accuracy and can capture the spatial-temporal correlation characteristics of traffic flow. Figure 7: Visualization of forecast results for METR-LA dataset. **(a)** The comparison curves under 15 min forecasting interval. **(b)** The comparison curves under 30 min forecasting interval. **(c)** The comparison curves under 60 min forecasting interval. **(d)** The curves of forecasting errors. After that, the effectiveness of dynamic graph reconfiguration algorithm will be discussed. First, the original graphs in PEMS-BAY dataset and METR-LA dataset consist of 325 and 207 traffic nodes, respectively, while employing the designed DGR algorithm. The numbers of traffic nodes in the reconfigured graph under different forecasting intervals are shown in Table 2. It can be seen that the number of traffic nodes are significant decreased under different forecasting intervals. Meanwhile, from Figures 6 and 7 and Table 2, it could be seen that the proposed EnGS-DGR performs better than other forecasting models with both the short-term and the long-term forecasting intervals. It is particularly necessary to point out that the number of traffic nodes is reduced remarkably with both the short-term and the long-term forecasting intervals.