A traffic flow forecasting method based on hybrid spatial–temporal gated convolution

Abstract

Influenced by the urban road network, traffic flow has complex temporal and spatial correlation characteristics. Traffic flow forecasting is an important problem in the intelligent transportation system, which is related to the safety and stability of the transportation system. At present, many researchers ignore the research need for traffic flow forecasting beyond one hour. To address the issue of long-term traffic flow prediction, this paper proposes a traffic flow prediction model (HSTGCNN) based on a hybrid spatial–temporal gated convolution. Spatial–temporal attention mechanism and Gated convolution are the main components of HSTGCNN. The spatial–temporal attention mechanism can effectively obtain the spatial–temporal features of traffic flow, and gated convolution plays an important role in extracting longer-term features. The usage of dilated causal convolution effectively improves the long-term prediction ability of the model. HSTGCNN predicts the traffic conditions of 1 h, 1.5 h, and 2 h on two general traffic flow datasets. Experimental results show that the prediction accuracy of HSTGCNN is generally better than that of Temporal Graph Convolutional Network (T-GCN), Graph WaveNet, and other baselines.