MSSTN: a multi-scale spatio-temporal network for traffic flow prediction

Abstract

Spatio-temporal feature extraction and fusion are crucial to traffic prediction accuracy. However, the complicated spatio-temporal correlations and dependencies between traffic nodes make the problem quite challenging. In this paper, a multi-scale spatio-temporal network (MSSTN) is proposed to exploit complicated local and nonlocal correlations in traffic flow for traffic prediction. In the proposed method, a convolutional neural network, a self-attention module, and a graph convolution network (GCN) are integrated to extract and fuse multi-scale temporal and spatial features to make predictions. Specifically, a self-adaption temporal convolutional neural network (SATCN) is first employed to extract local temporal correlations between adjacent time slices. Furthermore, a self-attention module is applied to capture the long-range nonlocal traffic dependence in the temporal dimension and fuse it with the local features. Then, a graph convolutional network module is utilized to learn spatio-temporal features of the traffic flow to exploit the mutual dependencies between traffic nodes. Experimental results on public traffic datasets demonstrate the superiority of our method over compared state-of-the-art methods. The ablation experiments confirm the effectiveness of each component of the proposed model. Our implementation on Pytorch is publicly available at https://github.com/csust-sonie/MSSTN.