## 2 Related Works
### Deep Learning Method  
As deep learning models are developing rapidly, RNN and its derivatives like LSTM and GRU have demonstrated excellent performance in time series tasks. [PERSON] et al. [23] pioneered the application of LSTM in predicting traffic speed, enhancing the modeling capabilities for long-sequence correlations and effectively addressing the gradient issues of disappearing and exploding. [PERSON] et al. [24] introduced a novel approach for forecasting traffic speed that utilizes stacked bidirectional and unidirectional LSTM to capture the correlations between preceding and subsequent time sequences. However, RNN models treat different road sequences as discrete data streams, focusing solely on the temporal relationships and neglecting the spatial relationships in traffic prediction. Additionally, RNN models suffer from slow convergence during training and struggle to effectively handle long time series data.  
To overcome these limitations, CNNs have been widely applied as an alternative approach to time series processing as seen in models like WaveNet [25] and TCN [26]. Recently, [PERSON] et al. introduced SICNet [27], which increased the receptive field of convolutional operations and utilized down-sampling interactive convolutions to enable multi-resolution analysis, achieving outstanding performance in traffic prediction. However, SCINet overlooks the spatial correlations present in traffic data. CNNs have been used in several research works to try to extract spatial correlations from traffic data. For instance, [PERSON] et al. [28] presented STResNet, which employs a CNN-based deep residual network for effective urban population flow forecasting. [PERSON] et al. [29] introduced a spatio-temporal dynamic network that combined CNN and LSTM to handle spatio-temporal information for taxi and bike flow prediction in New York. Nevertheless, CNNs rasterize graph data and fail to consider the unique topological structure of transportation networks, making them unsuitable for non-Euclidean traffic data and less effective in learning complex spatial relationships for traffic prediction tasks.  
Given the non-Euclidean characteristics of transportation networks, GCN has been extensively applied to traffic prediction tasks. GCN can be categorized into spectral-based GCN and frequency-based GCN. [PERSON] et al. [30] first introduced spectral-based generalized GCN, which utilized Fourier transformation to map the topological graph structure from spatial domain to spectral domain for convolutional operations and then reverse-transformed to finish the GCN process in the spatial domain. [PERSON] et al. [31] enhanced classical GCN by proposing ChebNet to simplify Laplacian computation. [PERSON] et al. [9] proposed a model called T-GCN for spatio-temporal traffic forecasting, employing GCN and GRU to separately learn temporal and spatial dependencies. [PERSON] et al. [8] presented STGCN, which utilized CNN to learn temporal relationships and GCN to model spatial relationships. [PERSON] et al. [32] introduced STG2 Seq, which incorporated two attention-based gated residual GCN modules to simulate spatio-temporal correlations. These methods, incorporating graph convolutional neural networks, directly perform convolutional operations on traffic data, leading to significant improvements in predictive performance.