DSTFGCN: A dynamic spatial-temporal fusion graph convolution network for traffic flow forecasting

Traffic flow prediction is one of the core technology of Intelligent Transportation System. Its fundamental challenge is to effectively model the complex spatial-temporal dependencies. Although extensive research has been conducted in this field, the limitations of current methods restrict their effectiveness in accurate predictions. For temporal dependence, existing methods based on recurrent neural networks only focus on local dependencies and ignore global dependencies. For spatial dependencies, existing methods use predefined or adaptive adjacency matrices that cannot accurately reflect the relationships between real traffic flow. To overcome these limitations, we propose a dynamic spatial-temporal fusion graph convolution network (DSTFGCN). In the temporal aspect, we introduce gated dilated causal convolution to capture the local dependencies and node-independent temporal graph convolution to capture the global dependencies specific to each node. In the spatial aspect, we propose a dynamic graph convolution block. It can construct dynamic graphs based on the characteristics of the input data and aggregate both local and global spatial dependencies. Experiments on six real-world datasets have shown that DSTFGCN outperforms current mainstream methods. The codes are available at https://github.com/SYLan2019/DSTFGCN.