STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting
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Cited by:
- Tongqiang Ding & Lianxin Zhang & Jianfeng Xi & Yingjuan Li & Lili Zheng & Kexin Zhang, 2023. "Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
- Yang, Di & Li, Hong & Wang, Peng & Yuan, Lihong, 2024. "Multistep traffic speed prediction: A sequence-to-sequence spatio-temporal attention model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
- Anton Agafonov & Alexander Yumaganov & Vladislav Myasnikov, 2023. "Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
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deep learning; traffic forecasting; graph convolution networks; attention mechanism; spatial–temporal graph data;All these keywords.
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