Multi-step ahead traffic speed prediction based on gated temporal graph convolution network

Abstract

Improving the traffic efficiency of the existing roads in the city has become an important task for the traffic management department. Timely and accurate traffic prediction is the key to urban traffic control and guidance. The multi-layer automatic feature extraction and expression ability of deep learning is deeply integrated with the non-linearity, multi-modality, and spatiotemporal correlation of traffic flow. A multi-step ahead traffic speed prediction based on gated temporal graph convolution network (GT-GCN) is proposed in this paper. Firstly, a dynamic weighted graph network is constructed according to the traffic road network structure, and then a graph convolutional network is used to process the graph structure data of the traffic road network and capture the spatial characteristics of the traffic flow. Subsequently, a gated temporal convolutional network is applied to capture the short- and long-range temporal correlations of traffic flow. Finally, a multi-step ahead prediction model based on a hybrid deep learning framework GT-GCN is proposed. Experiments using real datasets show that the proposed GT-GCN has a significant multi-step predictive performance compared to the baseline models.