ADGCN: An Asynchronous Dilation Graph Convolutional Network for Traffic Flow Prediction

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Spatial–temporal graph modeling plays an important role in the fields of transportation, meteorology, and social networks. Traffic flow prediction is a classic spatial–temporal modeling task. Existing methods usually do not take into account the asynchronous spatial–temporal correlation in traffic data. In addition, due to the complexity and variability of traffic data, long-term traffic forecasting is highly challenging. In order to solve the above problems, this article proposes a new deep learning-based asynchronous dilation graph convolution network (ADGCN) to model the spatial–temporal graphs. We mine the asynchronous spatial–temporal correlation in the traffic network, and propose the asynchronous spatial–temporal graph convolution (ASTGC) operation to extract this special relationship. Furthermore, we extend the dilated 1-D causal convolution to a graph convolution. The receptive field of the model increases exponentially with the increase of the network depth. Experiments are conducted on three public traffic data sets, and the results show that the prediction performance of ADGCN is better than the existing counterpart methods, especially in long-term prediction tasks.