T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 21; no. 9; pp. 3848 - 3858
• Main Authors: Zhao, Ling, Song, Yujiao, Zhang, Chao, Liu, Yu, Wang, Pu, Lin, Tao, Deng, Min, Li, Haifeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-09-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Data models; Dependence; Forecasting; Kalman filters; Logic gates; Predictive models; Roads; spatial dependence; Task analysis; temporal dependence; temporal graph convolutional network (T-GCN); Topology; Traffic control; Traffic forecasting; Traffic information; Traffic management; Traffic models; Traffic planning; Transportation networks; Transportation planning
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2935152

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an "open" scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN .