# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 1 Introduction
#### Related Work
The input of the model included the average speed and traffic flow upstream and downstream of the expressway, and the output was the passing time of vehicles. With the development of convolutional neural networks (CNN) in recent years, FNN is generally not used to obtain spatiotemporal correlation, but as an output aggregation or external data-processing sub-module. [PERSON] et al. [13] used FNN to combine the outputs of one CNN network and two long short-term memories (LSTM),and [PERSON] et al. [14] used FNN to learn road network features and add them to the model. However, the ability of convolutional neural networks to obtain time correlation is still insufficient. Further studies have combined long-term and short-term memory with convolutional neural networks, to extract spatial correlation by CNN and temporal correlation by LSTM. [PERSON] et al. [15] first used CNN to capture spatial features and added a residual neural unit to CNN to increase network depth, then used LSTM to capture temporal features, fusing the two parts to obtain traffic flow prediction results. [PERSON] et al. [16] used multi-layer LSTM and multi-layer CNN to process traffic flow data, and then fused the results obtained by the two sub-networks with a feedforward neural network. [PERSON] et al. [17] first used CNN to extract the data of each time slice as a feature vector, and then input it into LSTM for time-series modeling. On this basis, they proposed a spatiotemporal dynamic network for prediction of taxi demand, which could dynamically learn the correlations between locations. [PERSON] et al. [18] used a stacked autoencoder to encode traffic accident data, and LSTM to capture the time correlation of data. [PERSON] et al. [19] proposed the CONVLSTM model, which replaces all operations of ordinary LSTM with convolution operations, so can be used to process matrix sequences. [PERSON] et al. [20] proposed a heterogeneous traffic-accident prediction framework based on CONVLSTM. The framework adds a variety of auxiliary data, and divides the target area into three types: urban, rural, and mixed, and trains different models of different regions for fusion, to process heterogeneous spatiotemporal data. Because the traffic network is arranged in non-Euclidean space, but prediction models aim to use the convolution scheme that achieves excellent results in Euclidean space, the convolution for this kind of non-Euclidean space came into being. Figure 1 is a comparison of the differences between graph structure data and Euclidean spatial data. Convolution cannot be performed on graph structured data, but graph convolution can solve this problem. Graph convolution is a scheme developed in recent years, which is suitable for convolution operation of non-Euclidean spatial data. The two mainstream methods of graph convolution are the spatial method and the spectral method. The spatial method performs convolution filters directly on the nodes of the graph and their neighbors. Therefore, the core of this method is to select the neighbors of nodes [21]. A heuristic linear method selects the neighborhood of each central node, which has achieved good results in social network  
Figure 1: Comparison between Euclidean and non-Euclidean spatial data. tasks. [PERSON] et al. (2022) (Figure 1) proposed a general graphical convolution framework (GCN) based on the Laplace matrix, and then [PERSON] et al. (2023) optimized the method by using Chebyshev polynomials instead of a convolution kernel (chebnet). In recent years, GNN has been widely used in computer vision ([PERSON] et al., 2018; [PERSON] et al., 2020), recommendation systems ([PERSON] et al., 2018; [PERSON] et al., 2020), traffic prediction, and other fields. [PERSON] et al. (2020) proposed the DCRNN model, which uses bidirectional random walk on the graph and encoder-decoder architecture with planned sampling to obtain spatial correlation and temporal correlation, respectively. The STGCN model proposed by [PERSON] et al. (2020) uses a graph convolution network and Chebyshev network.