DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for
  Traffic Forecasting
Binqing Wu Ling Chen
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.