# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 1 Introduction
#### Related Work
Two graph neural networks and a one-dimensional CNN model spatial and temporal correlation, respectively, to form a spatiotemporal convolution module, and the final model is obtained by stacking two spatiotemporal convolution modules. [PERSON] et al. (2020) proposed a spatiotemporal attention mechanism, based on a graph convolution network and attention mechanism, to study the dynamic spatiotemporal correlation of traffic data. Spatial attention is used to capture the complex spatial correlation between different locations, and temporal attention is used to capture the dynamic correlation between different time slices. [PERSON] et al. (2020) indicated that STMGCN could calculate predictions of regional taxi demand. In their study, three graphs were constructed from the perspectives of connectivity between regions, location relationship between regions, and functional similarity between regions. The graph convolution network was used to extract the temporal correlation and spatial correlation from the three graph representations, and finally to calculate the average values to obtain prediction results. [PERSON], [PERSON], et al. (2020) proposed a heterogeneous spatiotemporal prediction framework for traffic prediction using incomplete historical data. This method uses a recurrent neural network to capture the time correlation and simultaneously estimate the missing values. At the same time, it models the dynamic correlation between road segments from aspects of their geography and history. It is also able to supplement the missing values. [PERSON] et al. (2018) proposed a denoising automatic encoder to generate a clearer version of the input signal, and optimized the model by adjusting the superparameter. This was shown to be an effective method for supplementing traffic data with missing data. [PERSON] et al. (2020) proposed a data interpolation method based on spatiotemporal probabilistic principal component analysis (PPCA), which was more robust for the supplementation of extreme missing values and successfully accounted for incomplete feature capture caused by missing values. [PERSON] et al. (2020) proposed a model of FSTGCN for fusing vehicle speed features to further capture spatial features, while building a dynamic adjacency matrix of multiple graphs to better explore hidden spatial information. There are several problems with the above methods: (1) Some only pay attention to the spatial correlation of traffic flow data and ignore the temporal correlation. (2) Others pay attention to the temporal correlation and ignore the spatial correlation. (3) In some methods, although the spatial and temporal correlation are calculated at the same time, the model for calculating the temporal correlation has a large number of parameters and requires large resource consumption, while the model for calculating spatial correlation pays too much attention to the high-frequency spatial feature information and ignores the low-frequency information. To solve such problems, we propose several strategies for effectively building models to simulate the temporal dynamics and spatial correlation of traffic flows. We used graph convolution to capture the spatial features of traffic flow. To avoid the high weight impact resulting from high-frequency data features, an LPF was designed in the graph convolution. This can suppress high-frequency data features and strengthen the low-pass filter of low-frequency data features, and thus make the signals on the whole graph smoother and improve their robustness. To address the inherent defects of recurrent networks, C-GLU was used on the time axis to capture the features in time series, and LSTM was not used. In this way, the training was accelerated and the efficiency was improved. Finally, the two parts were combined to extract spatial and temporal features. We validated our model by using two real datasets. The experiment results show that our model is better than the five existing typical models.