Multichannel spatial–temporal graph convolution network based on spectrum decomposition for traffic prediction

Abstract

Traffic prediction is an important topic in intelligent transportation systems (ITSs) that can provide support for many traffic applications. However, accurate traffic prediction is a challenging task, and its difficulties mainly come from the complex spatial and temporal dependencies of traffic network data. Previous studies mainly focused on capturing the spatiotemporal dependencies of traffic data but ignored the temporal frequency features of traffic flows. In this paper, we design a novel multichannel spatial–temporal graph convolution network based on spectrum decomposition (MSDGCN) for traffic prediction. First, we decompose the original traffic flow series into low-frequency, mid-frequency and high-frequency components based on spectrum analysis. Then, based on ChebNet and LSTM, a prediction model with three channels is constructed to capture the low-frequency, mid-frequency and high-frequency features of the traffic flow. Finally, an attention layer is adopted to assign weights for different channels, enabling the model to focus on capturing the main features of the traffic flow. Compared to the state-of-the-art baselines, our model captures the distinct frequency components of the original traffic flow series in a more meticulous manner. Experimental results obtained on six real-world datasets indicate the excellent performance of our model.