# Dynamic Causal Graph Convolutional Network for Traffic Prediction Source: [Dynamic Causal Graph Convolutional Network for Traffic Prediction]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:52:26.092Z Authors: Junpeng Lin, Ziyue Li, Zhishuai Li, Lei Bai, Rui Zhao, Chen Zhang Published: 2023-06-12T10:46:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Traffic Forecasting on METR-LA Full (test)](): Traffic Forecasting; METR-LA Full (test); MAE: 2.73. - [Traffic Forecasting on METR-LA 20 nodes (test)](): Traffic Forecasting; METR-LA 20 nodes (test); MAE: 2.72. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()