# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 5 Experiments
### Experiment Results  
The experimental results comparing DSTSAN with other baseline methods on four real datasets are presented in Table 2. The best results are highlighted in bold. Based on the analysis of Table 2, the following conclusions can be drawn:  
1. Methods incorporating neural networks, Graph Neural Networks, and attention mechanisms generally outperform traditional time series forecasting models such as VAR and ARIMA. This is attributed to the fact that traditional time series forecasting models often rely on static assumptions, which are insufficient for capturing the highly nonlinear spatial dependencies present in the traffic data. Consequently, these traditional models tend to overlook crucial information, resulting in an unsatisfactory prediction performance;  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**PEMS03**} & \multicolumn{3}{c}{**PEMS04**} & \multicolumn{3}{c}{**PEMS07**} & \multicolumn{3}{c}{**PEMS08**} \\ \cline{2-13}  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline VAR & 24.68 & 37.91 & 21.62\% & 25.35 & 39.13 & 19.67\% & 52.50 & 76.28 & 34.73\% & 24.31 & 35.64 & 16.32\% \\ ARIMA & 34.47 & 46.53 & 29  
2. Graph Neural Network models, such as STGNN and Graph WaveNet, exhibit substantial advantages over neural network models like FC-LSTM and DCRNN in terms of prediction performance. This is attributed to the ability of Graph Neural Network models to effectively model both local and global spatial dependencies present in the traffic data, enabling them to capture a richer set of essential information compared to the aforementioned models. Furthermore, attention-based methods outperform Graph Neural Network-based methods. This can be attributed to the superior capability of attention mechanisms in integrating dynamic spatiotemporal dependencies present in the traffic data;
3. It is not universally true that methods based on Graph Neural Networks or attention mechanisms are always superior to other approaches. For instance, the STG-NCDE model, which combines neural controlled differential equations with Graph Convolutional Network models, exhibits comparable performance and competitiveness. This suggests that there is diversity in the solutions for the same problem, and different approaches can yield competitive results;
4. This model demonstrates a superior performance compared to other baseline methods for several reasons. Firstly, a comprehensive consideration of both local and global spatial dependencies is incorporated in the spatial dimension. Additionally, in the temporal dimension, this method accounts for the multi-periodicity of traffic flow. This holistic approach to spatial and temporal factors contributes to the improved performance of this model. Secondly, a feature augmentation layer is introduced that automatically adjusts the information interaction among different features. This feature augmentation facilitates the enhancement of representation capabilities for each feature dimension, resulting in an improved overall performance. Lastly, this model utilises two spatiotemporal attention modules to effectively capture and integrate the dynamic spatiotemporal dependencies present in the traffic data. This enables the model to accurately capture the complex patterns and relationships within the data, leading to more precise predictions.