DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting
This addresses traffic forecasting for intelligent transportation systems, representing an incremental improvement by focusing on cross dependencies.
The paper tackles traffic forecasting by proposing DSTCGCN to learn dynamic spatial-temporal cross dependencies jointly via graphs, achieving state-of-the-art performance on six real-world datasets.
Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network. Existing works usually learn spatial and temporal dependencies separately, ignoring the dependencies crossing spatial and temporal dimensions. In this paper, we propose DSTCGCN, a dynamic spatial-temporal cross graph convolution network to learn dynamic spatial and temporal dependencies jointly via graphs for traffic forecasting. Specifically, we introduce a fast Fourier transform (FFT) based attentive selector to choose relevant time steps for each time step based on time-varying traffic data. Given the selected time steps, we introduce a dynamic cross graph construction module, consisting of the spatial graph construction, temporal connection graph construction, and fusion modules, to learn dynamic spatial-temporal cross dependencies without pre-defined priors. Extensive experiments on six real-world datasets demonstrate that DSTCGCN achieves the state-of-the-art performance.