  Complex spatial dependencies in transportation networks make traffic
prediction extremely challenging. Much existing work is devoted to learning
dynamic graph structures among sensors, and the strategy of mining spatial
dependencies from traffic data, known as data-driven, tends to be an intuitive
and effective approach. However, Time-Shift of traffic patterns and noise
induced by random factors hinder data-driven spatial dependence modeling. In
this paper, we propose a novel dynamic frequency domain graph convolution
network (DFDGCN) to capture spatial dependencies. Specifically, we mitigate the
effects of time-shift by Fourier transform, and introduce the identity
embedding of sensors and time embedding when capturing data for graph learning
since traffic data with noise is not entirely reliable. The graph is combined
with static predefined and self-adaptive graphs during graph convolution to
predict future traffic data through classical causal convolutions. Extensive
experiments on four real-world datasets demonstrate that our model is effective
and outperforms the baselines.
