## 2 Related Works  
Traffic forecasting necessitates the consideration of both temporal correlation and spatial dependency. In recent years, the application of deep learning to traffic flow forecasting, which is used to represent spatial-temporal correlation, has seen the emergence of methods that can be categorized as RNN-based, GCN-based, and those based on attention mechanisms. **RNN-based:** Given the exceptional predictive capabilities of Recurrent Neural Networks (RNN) [7] and their variants, such as Long Short-Term Memory networks (LSTM) [8] and Gated Recurrent Units (GRU), these have been extensively utilized for time series forecasting tasks, including traffic flow prediction. The Conv-LSTM model [9] was developed by integrating Convolution and bi-directional LSTM to address the issue of long-term dependencies. Furthermore, a multi-dimensional LSTM network [10; 11; 12] was designed to capture both temporal and spatial correlations in traffic flows, demonstrating superior performance when compared experimentally with other prominent models. The Diffusion Convolutional Recurrent Neural Network (DCRNN) [13] employs bidirectional random drifts on the graph to capture spatial dependencies, and an encoder-decoder structure to capture temporal dependencies. **GCN-based:** Multiple research studies have utilized Convolutional Neural Networks (CNN) for spatial modelling to characterize the spatial correlations of traffic flow. However, CNN has limitations when applied to non-Euclidean spaces. To overcome this, graph convolution has been employed to model the spatial correlations of complex road networks. STGCN [14] proposed a combination of Graph Convolutional Networks (GCN) and Recurrent Neural Networks (RNN) for traffic flow prediction. This approach utilizes graph convolution to extract spatial features and temporal gated convolution to extract temporal features. T-GCN [3] introduced temporal graph convolution networks, which leverage GRU to address the issues of gradient explosion and disappearance during training. It combines graph convolution with GRU to extract spatial and temporal features. Graph WaveNet (GWN) [15] presented a novel graph neural network model for spatial-temporal graph modelling, capturing hidden spatial dependencies accurately. Considering the periodicity of traffic flow data, MCSTGCN [16] proposed multi-component spatial-temporal graph convolutional networks. The network is divided into three components to extract temporal-dependent features at recent, daily, and weekly intervals. STSGCN [17] proposed spatial-temporal synchronous graph convolutional networks to predict traffic flow by constructing local spatial-temporal graphs, integrating information across nodes at different time points. AGCRN [18] devised a new GCN parameter factorization module and combined it with a recurrent network for traffic forecasting. MTGNN [19] utilized the innate dependencies among multiple road segments for traffic forecasting. Consequently, the GCN-based method, which captures temporal correlation using RNN and spatial dependency using graph convolution, has gained popularity as a model for traffic flow forecasting due to its exceptional representation capacity. **Attention mechanism-based:** Attention mechanisms enable convolutional neural networks to dynamically focus on important information. In 2018, GAT [20] first combined the attention mechanism with a graph structure. The Graph Attention LSTM Network [21], on the other hand, combines the graph attention mechanism with the LSTM network. This combination allows for learning the spatial features of the graph and using LSTM to predict and fit the prediction values. TPA-LSTM [22] improves traffic flow prediction by optimizing the hidden state output in the LSTM network and then utilizing the attention mechanism to extract key time nodes. Similarly, ConsTGAT [23] employs the attention mechanism and a convolutional approach to learn external characteristics and extract spatial correlation, ultimately enhancing vehicle arrival time estimation on the Baidu map.