  Modeling complex spatiotemporal dependencies in correlated traffic series is
essential for traffic prediction. While recent works have shown improved
prediction performance by using neural networks to extract spatiotemporal
correlations, their effectiveness depends on the quality of the graph
structures used to represent the spatial topology of the traffic network. In
this work, we propose a novel approach for traffic prediction that embeds
time-varying dynamic Bayesian network to capture the fine spatiotemporal
topology of traffic data. We then use graph convolutional networks to generate
traffic forecasts. To enable our method to efficiently model nonlinear traffic
propagation patterns, we develop a deep learning-based module as a
hyper-network to generate stepwise dynamic causal graphs. Our experimental
results on a real traffic dataset demonstrate the superior prediction
performance of the proposed method. The code is available at
https://github.com/MonBG/DCGCN.
