A deep marked graph process model for citywide traffic congestion forecasting

Forecasting citywide traffic congestion on large road networks has long been a nontrivial research problem due to the challenge of modeling complex evolution patterns of congestion in highly stochastic traffic environments. Arguing that purely data-driven methods may not perform well for congestion forecasting, we propose a deep marked graph process model for predicting the congestion indices and the occurrence time of traffic congestion events for complex signalized road networks. Traffic congestion is considered as a nonrigorous spatiotemporal extreme event. We extend the traditional point process model by integrating a specially designed spatiotemporal graph convolutional network. This hybrid strategy takes advantage of the simple form of the point process model as well as the ability of graph neural networks to emulate the evolution of congestion. Experiments on real-world congestion data sets show that the proposed method outperforms state-of-the-art baseline methods, yielding satisfactory prediction results on a large signalized road network with superior computational efficiency.

Automated summary

This study proposes a deep marked graph process model for predicting traffic congestion on complex signalized road networks. By integrating a specially designed spatiotemporal graph convolutional network, this model can effectively emulate the evolution of congestion. Experimental results demonstrate that the proposed method outperforms existing methods in terms of prediction accuracy and computational efficiency.