# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 4 Experiments
### Ablation Study  
To validate each component of the proposed AMGC-AT model, we performed further ablation studies. We compared our model to eight carefully designed variants. Although some changes were made, all variants have the same framework and parameter settings. Performance on the Hangzhou datasets is shown in Table 4.  
Figure 8: Prediction results. 1. AMGC-S: This variant eliminates the temporal self-attention mechanism and uses representations learned from spatial graphs directly to infer traffic flow. 2. AMGC-T: This variant eliminates the spatial self-attention mechanism, while the other components remain the same, using only spatial features derived from multi-graph convolution to infer traffic flow. 3. AGC-AT: This variant eliminates the convoluted components of Multi-graph and does not use predefined graph representations based on a priori knowledge constructs for feature extraction, leaving the components unchanged. 4. AMG-AT: This variant removes the Multi-graph shared parameter section in the Multi-graph convolution component, which assumes that there is not sufficient correlation between predefined graph representations. The remaining components, including the spatiotemporal attention component and the causal convolution output component, remain the same. 5. AGC1-AT: This variant removes the schematic representation of a station-based POI information construct in a multi-graph convolution component, leaving the rest of the component unchanged. 6. AGC2-AT: This variant removes graphical representations of convoluted components based on station traffic volume similarity, leaving the rest of the component unchanged. 7. AGC3-AT: This variant eliminates the schematic representation of the simrank-based convolution component, leaving the rest of the component unchanged. 8. AMGC-AT1: This variant eliminates the periodic, short- and long-term division of traffic flow in the temporal and spatial self-attention component, with traffic flow information fed directly into the temporal and spatial self-attention component and the rest remaining the same. 9. AMGC-AT2: This variant eliminates the short- and long-term division of traffic flows in the temporal and spatial self-attention segments; only the adjacent traffic flows are fed into the temporal and spatial self-attention segments, and the rest remain the same. 10. AMGC-AT3: This variant eliminates the adjacent division of traffic flow in the temporal and spatial self-attention segments and only inputs traffic flow containing short-and long-term information into the temporal and spatial self-attention segments, leaving the remainder unchanged. 11. AMGC-AT: The complete model presented in this paper. As shown in Table 4, the experiment to verify the validity of each predefined graph of the multi-graph convolution component clearly demonstrates that the performance of the whole model decreases to varying degrees when removing any graph, which shows that the representation of the graph based on a priori knowledge is effective. Secondly, in the AMG-AT model, after removing the convoluted portion of the graph with shared parameters, the performance of the model decreased, suggesting that there is common \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time Interval**} & \multicolumn{3}{c}{**10 Min**} \\ \cline{2-4}  & **RMSE** & **MAE** & **WMAPE** \\ \hline AMGC-S & 28.81 & 31.28 & 17.20\% \\ AMGC-T & 28.56 & 29.30 & 15.58\% \\ AGC-AT & 29.53 & 19.56 & 13.01\% \\ AMG-AT & 26.29 & 16.71 & 11.24\% \\ AGC1-AT & 25.97 & 16.31 & 11.04\% \\ AGC2-AT & 27.02 & 17.78 & 11.95\% \\ AGC3-AT & 26.81 & 17.74 & 12.49\% \\ AMGC-AT1 & 26.45 & 16.94 & 11.65\% \\ AMGC-AT2 & 25.85 & 15.53 & 8.99\% \\ AMGC-AT3 & 26.47 & 15.74 & 9.18\% \\ AMGC-AT & 25.32 & 16.65 & 10.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study. information between graph representations based on different a priori knowledge that are not completely unrelated.