Spatio-temporal graph attention networks for traffic prediction-Reference-Cited by-同舟云学术

Spatio-temporal graph attention networks for traffic prediction

Published:2023-09-30 Issue: Volume: Page:1-11
ISSN:1942-7867
Container-title:Transportation Letters
language:en
Short-container-title:Transportation Letters

Author:

Ma Chuang¹,Yan Li¹,Xu Guangxia²

Affiliation:

1. Department of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China

2. Advanced Institute of Cyberspace Technology, Guangzhou University, Guangzhou, China

Funder

National Natural Science Foundation of China

Research Program of Basic Research and Frontier Technology of Chongqing

Key R & D plan of Hainan Province

Technology Innovation and Application Development Projects of Chongqing

Publisher

Informa UK Limited

Subject

Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/19427867.2023.2261706

Reference42 articles.

1. Adaptive Graph Con- Volutional Recurrent Network for Traffic Forecasting;Bai L.;Advances in neural information processing systems,2020

2. Machine Learning-based traffic prediction models for Intelligent Transportation Systems

3. Dai, R., S. K. Xu, Q. Gu, C. G. Ji, K. K. Liu. 2020. “Hybrid Spatio- Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, United States, 3074–3082.

4. Geng, X., Y. G. Li, L. Y. Wang, L. Y. Zhang, Q. Yang, J. P. Ye, Y. Liu. 2019. “Spatiotemporal Multi-Graph Convolution Network for Ride- Hailing Demand Forecasting.” In Proceedings of the AAAI conference on artificial intelligence, Honolulu, Hawaii, USA, 33, 3656–3663.

5. Grover, A., J. Leskovec. 2016. “node2vec: Scalable Feature Learning for Networks.” In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, United States, 855–864.

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