Forecasting nationwide passenger flows at city-level via a spatiotemporal deep learning approach
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DOI: 10.1016/j.physa.2021.126603
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Cited by:
- Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
- Luo, Jie & Wen, Chao & Peng, Qiyuan & Qin, Yong & Huang, Ping, 2023. "Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
- Hu, Yi-Chung, 2023. "Air passenger flow forecasting using nonadditive forecast combination with grey prediction," Journal of Air Transport Management, Elsevier, vol. 112(C).
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Keywords
Passenger flow forecasting; Spatiotemporal residual network; Irregular-shaped region; Deep learning;All these keywords.
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