## 4 RNN Structures Used for Traffic Prediction
### Hybrid Models Including RNN Techniques
#### 4.4.2 RNNs + CNNs  
Researchers have gradually recognized the importance of spatial dependency, and the combined use of RNNs and CNNs in addressing spatiotemporal data issues, particularly in traffic flow prediction in the Euclidean-based space, has become an important research direction. This fusion method fully leverages the advantages of RNNs in handling time series data and the efficiency of CNNs in processing spatial features. RNNs are particularly suitable for handling time series data because they can capture the temporal dynamic characteristics and long-term dependencies in the data. However, RNNs have limited capability in dealing with high-dimensional spatial data. CNNs effectively identify and extract local features from high-dimensional spatial data, such as images and videos. Still, they do not directly handle dynamic information in time series data. Therefore, combining  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hybrid Models** & **Reference** & **Techniques** \\ \hline RNNs + Traditional ML & [69] & LSTM + K-NN \\ Techniques & [70] & GRU + K-NN \\ \hline RNNs + CNNs & [73] & LSTM + CNN \\ \hline RNNs + CNNs & [74] & GRU + GCN \\ RNNs + GNNs & [75] & LSTM + GNN \\  & [76] & LSTM + GNN \\ \hline RNNs + Attention & [77] & LSTM + Attention \\ RNNs + RNN & [79] & LSTM + Attention \\  & [80] & RNN + GNN + Attention \\  & [81] & GRU + GCN + Attention \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of different hybrid models.  
RNNs and CNNs can achieve more comprehensive and accurate modeling and prediction of spatiotemporal data. Figure 6 shows an example of a hybrid RNN and CNN model.  
[PERSON] et al. [33] transferred the application of CNNs from images to the field of traffic prediction, forecasting large-scale transportation networks by utilizing spatiotemporal traffic dynamics transformed into images. This method integrates the spatial and temporal dimensions of traffic data into a unified framework, involving the conversion of traffic dynamics into a two-dimensional spatiotemporal matrix, fundamentally treating traffic flow as images. This matrix captures the intricate relationships between time and space in traffic flow, allowing for a more nuanced analysis of traffic patterns. [PERSON] et al. [73] proposed an innovative approach for predicting traffic flow in large-scale transportation networks using Spatiotemporal Recurrent Convolutional Networks (SRCNs). The particular distinction of this method lies in transforming network-wide traffic speeds into a series of static images, which are then employed as inputs for the deep learning architecture. This image-based representation allows for a more intuitive and effective capture of the complex spatial relationships inherent in traffic flow across the transportation network. Empirical testing on a Beijing transportation network comprising 278 links further demonstrated the effectiveness of this method. Due to the potential variation in spatial dependencies between different locations in the road network over time and the non-periodic nature of temporal dynamics, [PERSON] et al. [74] proposed a novel Spatial-Temporal Dynamic Network (STDN) for traffic prediction. This approach utilizes a flow gating mechanism to track the dynamic spatial similarity between regions, enabling the model to understand how traffic flows between areas change over time, which is crucial for accurately predicting future traffic volumes. Additionally, they employ a periodically shifted attention mechanism to handle long-term periodic information and time offsets. This allows the model to account for subtle variations in daily and weekly patterns, such as shifts in peak traffic times, thereby enhancing prediction accuracy.