A deep marked graph process model for citywide traffic congestion forecasting |
| |
| Authors: | Tong Zhang Jianlong Wang Tong Wang Yiwei Pang Peixiao Wang Wangshu Wang |
| |
| Affiliation: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;2. Changjiang Space Information Technology Engineering Co., Ltd. (Wuhan), Wuhan, China;3. School of Geodesy and Geomatics, Wuhan University, Wuhan, China;4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;5. Research Unit Cartography, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria |
| |
| Abstract: | 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. |
| |
| Keywords: | |
|
|
