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For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand.
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K. Jin, W. Wang, S. Li, P. Liu, and H. Sun, “Dockless shared-bike demand prediction with temporal convolutional networks,” in CICTP 2020 , 2020, pp. 2851–2863
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H. Xu, T. Zou, M. Liu, Y. Qiao, J. Wang, and X. Li, “Adaptive spatiotemporal dependence learning for multi-mode transportation demand prediction,” IEEE Transactions on Intelligent Transportation Systems , 2022
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Y. Liang, G. Huang, and Z. Zhao, “Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach,” Transportation Research Part C: Emerging Technologies , vol. 140, p. 103731, 2022
2022
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2022
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Y. Liang, G. Huang, and Z. Zhao, “Bike sharing demand prediction based on knowledge sharing across modes: A graph-based deep learning approach,” accepted by 2022 IEEE 25th international conference on intelligent transportation systems , 2022
2022
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Y. Wang, Z. Zhan, Y. Mi, A. Sobhani, and H. Zhou, “Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity,” Transportation Research Part D: Transport and Environment , vol. 104, p. 103194, 2022
2022
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