Abstract
The accurate prediction of short-term passenger flow is of high importance to efficiently manage the passenger flow of metro systems and adjust timetable accordingly. However, the existing methods of passenger flow prediction cannot achieve adequate accurate results due to its complex nonlinear spatiotemporal characteristics. To improve the accuracy of short-term passenger flow prediction, this paper proposes a deep learning model based on a spatiotemporal framework. Firstly, the graph convolutional network, which incorporates prior domain knowledge (such as travel time and origin–destination demand), is used to extract spatial features of passenger flow. Secondly, the attention mechanism is integrated into the gated recurrent unit to extract the time correlation of passenger flow. Finally, external factors are introduced to capture their impact on passenger flow as well. A case study of the Beijing Subway system is illustrated to verify the performance of the proposed model. The results show that compared with the existing models, the proposed model achieves the highest prediction accuracy and strong robustness. Furthermore, we demonstrate that the adjacency matrix based on travel time outperforms the one based on OD demand, especially during evening peak hours. In addition, it is also verified that the attention mechanism and external factors can improve the prediction performance of the proposed model.
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The authors have not disclosed any funding. This project was supported by the National Natural Science Foundation of China (No.71871012) and the Natural Science Foundation of Beijing (No. 9212014).
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XW: was involved in design of methodology; programming; writing the initial draft. XX: helped in critical review, commentary and revision; oversight and leadership responsibility for the research activity planning and execution. YW: and JL contributed to critical review, commentary, and revision.
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Wang, X., Xu, X., Wu, Y. et al. An effective spatiotemporal deep learning framework model for short-term passenger flow prediction. Soft Comput 26, 5523–5538 (2022). https://doi.org/10.1007/s00500-022-07025-8
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DOI: https://doi.org/10.1007/s00500-022-07025-8