Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 9; pp. 16137 - 16147
• Main Authors: Wang, Hanqiu, Zhang, Rongqing, Cheng, Xiang, Yang, Liuqing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Correlation; Data models; Graph convolutional network; intersection without historical data; Predictive models; Protocols; Roads; spatial-temporal dependence; Support vector machines; Traffic flow; traffic flow prediction; Traffic information; Traffic intersections; Traffic management; Traffic planning; Urban areas
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3148105

In recent years, traffic flow prediction has attracted more and more interest from both academia and industry since such information can provide effective guidance for traffic management or driving planning and enhance traffic safety and efficiency. But due to the complicated spatial-temporal dependence in actual roads and the limitation of intersection monitoring equipment, there are still many challenges in spatial-temporal traffic flow prediction. In this paper, we propose a novel hierarchical traffic flow prediction protocol based on spatial-temporal graph convolutional network (ST-GCN), which incorporates both spatial and temporal dependence of intersection traffic to achieve a more accurate traffic flow prediction. Different from existing works, our proposed protocol with the Adjacent-Similar algorithm can also effectively predict the traffic flow of the intersections without historical data. Experiments based on practical traffic data of the city of Qingdao, China demonstrate that our proposed ST-GCN-based traffic flow prediction protocol outperforms the state-of-the-art baseline models. Moreover, as for the intersections without historical data, we can also obtain a good prediction accuracy.