## 4 RNN Structures Used for Traffic Prediction
### Hybrid Models Including RNN Techniques
#### 4.4.4 RNNs + Attention  
Accurate traffic prediction optimizes road network operational efficiency, enhances traffic safety, and reduces environmental pollution. In recent years, with the development of deep learning methods, RNNs have been widely applied in traffic prediction due to their excellent ability to handle temporal data. Meanwhile, introducing attention mechanisms further improves the model's capability to capture temporal and spatial dependencies. Figure 8 shows an example of a hybrid RNN and attention model.  
The STAGCN model proposed by [PERSON] et al. [63] combined static and dynamic graphs to accurately capture spatial dependencies in traffic networks. Additionally, the model introduces gated temporal attention modules to effectively handle long-term dependencies in time series data, thereby improving traffic prediction accuracy. Some scholars have improved training efficiency by introducing attention mechanisms on top of LSTM; for example, [PERSON] et al. [77] integrated attention mechanisms to enhance training efficiency by simplifying the structure of LSTM networks while focusing on the most influential features for current predictions. This approach has shown superior performance and speed over traditional LSTM and RNN models on multiple public datasets. Similarly, [PERSON] et al. [78] improved the LSTM-RNN by introducing attention mechanisms and developed a short  
Figure 7: Example of a hybrid RNN and GNN model.  
term traffic flow prediction model. This model demonstrates efficiency and accuracy in practical applications with real traffic flow data, confirming the effectiveness of attention mechanisms in enhancing traffic prediction performance. The SA-LSTM model proposed by [PERSON] et al. [79] utilized self-attention mechanisms, effectively addressing the vanishing gradient problem and accurately capturing the spatiotemporal characteristics of traffic information. The superiority of this model is demonstrated in experiments on Shenzhen road network data and floating car data, showcasing the potential of self-attention mechanisms in improving traffic prediction accuracy. Additionally, some scholars, both domestically and abroad, have combined dynamic spatiotemporal graph recurrent networks with attention mechanisms. For instance, the Dynamic Spatiotemporal Graph Recurrent Network (DSTGRN) model proposed by [PERSON] et al. [80] integrated spatial attention mechanisms and multi-head temporal attention mechanisms by encoding road nodes, providing a fine-grained perspective for modeling the temporal dependencies of traffic flow data. This model surpasses its baseline models in prediction accuracy, demonstrating the potential application of dynamic graph networks in traffic prediction. [PERSON] et al. [81] effectively captured the spatiotemporal characteristics of road conditions and achieved precise traffic speed prediction by combining the GCN and GRU and introducing multi-head attention mechanisms. Testing results on two real datasets demonstrate the superior performance of this model, further validating the application value of multi-head attention mechanisms in traffic prediction.