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.

翻译：对相关交通序列中复杂的时空依赖关系进行建模是交通预测的关键。尽管近期研究通过使用神经网络提取时空相关性取得了更好的预测性能，但其有效性取决于用于表示交通网络空间拓扑的图结构质量。本研究提出了一种新颖的交通预测方法，通过嵌入时变动态贝叶斯网络来捕获交通数据精细的时空拓扑结构，进而采用图卷积网络生成交通预测。为高效建模非线性交通传播模式，我们开发了一种基于深度学习的超网络模块，用于生成逐步动态因果图。在真实交通数据集上的实验结果表明，所提方法具有优越的预测性能。相关代码已开源至 https://github.com/MonBG/DCGCN。