Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 7; pp. 7359 - 7370
• Main Authors: Yu, James J. Q., Markos, Christos, Zhang, Shiyao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Correlation; Data correlation; Data mining; Deep learning; Forecasting; Information retrieval; intelligent transportation systems; Internet of Things; long-term forecast; Predictive models; Spatiotemporal data; Traffic management; Traffic speed; Traffic speed prediction; Training; Transportation
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3069234

Traffic speed prediction is among the foundations of advanced traffic management and the gradual deployment of internet of things sensors is empowering data-driven approaches for the prediction. Nonetheless, existing research studies mainly focus on short-term traffic prediction that covers up to one hour forecast into the future. Previous long-term prediction approaches experience error accumulation, exposure bias, or generate future data of low granularity. In this paper, a novel data-driven, long-term, high-granularity traffic speed prediction approach is proposed based on recent development of graph deep learning techniques. The proposed model utilizes a predictor-regularizer architecture to embed the spatial-temporal data correlation of traffic dynamics in the prediction process. Graph convolutions are widely adopted in both sub-networks for geometrical latent information extraction and reconstruction. To assess the performance of the proposed approach, comprehensive case studies are conducted on real-world datasets and consistent improvements can be observed over baselines. This work is among the pioneering efforts on network-wide long-term traffic speed prediction. The design principles of the proposed approach can serve as a reference point for future transportation research leveraging deep learning.