Dynamic Causal Graph Convolutional Network for Traffic Prediction
This work addresses traffic prediction for urban planning and management, but it is incremental as it builds on existing graph-based neural network approaches.
The paper tackles traffic prediction by modeling complex spatiotemporal dependencies, proposing a method that embeds dynamic Bayesian networks and graph convolutional networks to capture fine spatiotemporal topology, resulting in superior prediction performance on a real traffic dataset.
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.