## 2 Related Work  
Spatio-temporal prediction refers to the prediction of unknown system states in spatial and temporal dimensions and is widely used in many real-world scenarios, such as weather prediction, traffic prediction, and earthquake prediction. Traffic prediction is a typical spatio-temporal prediction problem [2; 9]. Traditional traffic prediction utilized model-driven methods, such as Historical Average (HA) [19] and Autoregressive Integrated Moving Average (ARIMA). These models are based on the linear analysis method, which are less accurate in complex nonlinear traffic prediction. Subsequently, data-driven methods gradually became mainstream, and machine learning methods were used in the early days, such as Vector Auto Regression (VAR) [20], Support Vector Regression (SVR) [21], and K-Nearest Neighbor (KNN) [22], but they required more detailed engineering features and were more complex in terms of time. With the rapid development of deep learning technology, Recurrent Neural Networks (RNNs) have been widely used in time series prediction tasks [23; 24; 25]. However, RNNs cannot consider the correlation of nodes in the spatial dimension. As such, Convolution Neural Networks (CNNs) for image processing have become a popular research topic, which divides the space into grids for convolution to capture spatial traffic dependencies [26]. However, regularized grid data is unable to properly reflect irregular traffic network structure information. Therefore, Graph Convolutional Networks (GCNs) [4; 5; 6; 7] have received extensive attention and are widely used in traffic prediction to extract spatial dependencies. GCN is divided into spectral domain graph convolution [4] and spatial domain graph convolution [5; 6; 7; 27]. The spectral domain graph convolution uses Fourier transform to convert the graph signal to the spectral domain, followed by the convolution operation with a rather complicated calculation. The spatial domain graph convolution directly defines the neighborhood nodes of the convolution on the graph followed by the convolution operation, which is more intuitive and flexible. For example, GraphSage [6] introduces an aggregation function to aggregate the neighbor information of nodes. GAT [7] uses an attention mechanism to determine the importance of each neighbor node. DCNN [27] regards graph convolution as a diffusion process with graph convolution realized via the probability transition matrix between nodes. GCN uses information regarding graph edges to aggregate node information for generating a new node representation, so the adjacency matrix representing the edge relationship is particularly important. In a traffic network graph, an adjacency matrix is usually constructed based on the distance or connectivity between nodes. DCRNN [8] models the traffic flow as a diffusion process on a directional graph, which constructs an adjacency matrix based on the distance map and uses diffusion graph convolution to extract the spatial representation. STGCN [9] builds an adjacency matrix based on the distance graph, with spectral domain GCN used to extract the spatial representation. HetGAT [16] builds an adjacency matrix based on the distance graph between nodes and the relationship between nodes and sites was also built and used GAT to extract the spatial representation. AST-MTL [18] builds an adjacency matrix based on the connectivity between nodes and the spatial representation was extracted using spectral domain GCN. A simple distance graph extracts the spatial relationship between nodes and it is easy to ignore the correlation between nodes. STSGCN [11] connects adjacent nodes to directly capture the local correlation between close nodes and their spatio-temporal neighbors, which placed limitations on capturing correlations between far nodes. HGCN [28] builds an adjacency matrix based on the distance graph and uses the distance between nodes to generate a regional graph for obtaining node similarity.