Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
Author:
Oluwasanmi Ariyo1ORCID, Aftab Muhammad Umar2ORCID, Qin Zhiguang1ORCID, Sarfraz Muhammad Shahzad2, Yu Yang3ORCID, Rauf Hafiz Tayyab4ORCID
Affiliation:
1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China 2. Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan 3. Centre for Infrastructure Engineering and Safey, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia 4. Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
Abstract
Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference49 articles.
1. Zheng, C., Fan, X., Wang, C., and Qi, J. (2020, January 7–12). GMAN: A Graph Multi-Attention Network for Traffic Prediction. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 20), New York, NY, USA. 2. Chen, J., Liao, S., Hou, J., Wang, K., and Wen, J. (2020, January 11–14). GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada. 3. Yao, H., Tang, X., Wei, H., Zheng, G., and Li, Z. (February, January 27). Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’19), Honolulu, HI, USA. 4. Dai, R., Xu, S., Gu, Q., Ji, C., and Liu, K. (2020, January 6–10). Hybrid Spatio- Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Long Beach, CA, USA. 5. Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning;Tawfeek;Transp. Res. Rec.,2019
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