FreqST: Exploiting Frequency Information in Spatiotemporal Modeling for Traffic Prediction

The goal of traffic prediction is to predict future traffic values such as speeds or flows in a transportation network. Existing studies mainly focus on exploring spatiotemporal dependencies in time and space domains. However, they generally neglect the frequency information encoded in the traffic data, which sheds light on the true spatiotemporal correlations among any vertices in the transportation network. In this paper, we aim to incorporate two kinds of frequency information, timefrequency and space-frequency, into the modeling of spatiotemporal dependencies towards more accurate traffic prediction. We investigate the importance of two frequencies using real traffic data and develop an end-to-end neural approach to utilizing both frequencies for traffic prediction effectively. Specifically, we propose an attention module to aggregate features from semantically related vertices according to their time-frequency similarities, and derive a semantic representation. A novel spacefrequency-based gating mechanism is designed to control how much local information is propagated to the state updating, enforced with the graph convolution to generate an effective local representation. Finally, our model combines the semantic and local representations with gated recurrent units to predict future traffic values. Extensive experiments on two real-world traffic datasets demonstrate that our proposed approach yields better prediction performance than the state-of-the-art methods.