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Learning spatial-temporal dynamics and interactivity for short-term passenger flow prediction in urban rail transit

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Abstract

Accurate short-term passenger flow prediction in urban rail transit is critical in ensuring the stable operation of urban rail systems. However, accurate passenger flow prediction still faces challenges, including modeling the dynamics of passenger flow data in spatial and temporal dimensions and capturing the interactions between the inflows and outflows. To solve these problems, a novel model called the multi-feature fusion graph convolutional network (MFGCN) is proposed. Firstly, parallel graph branch networks are established to describe inflow and outflow information from geographic and semantic perspectives. Then, in the spatial dimension, the graph convolutional networks with spatial attention are designed to learn the dynamic spatial correlations of nodes in the two graphs. In the temporal dimension, the long short-term memory networks with temporal attention are developed to learn the dynamic temporal dependencies of passenger flow data. Finally, a three-dimensional residual network is established to capture the spatial-temporal interactive dependencies between inflows and outflows. Experiments on Nanning Metro Line 1 passenger flow datasets demonstrated that MFGCN outperformed the existing baseline models, which could provide technical support for URT network operation management.

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Data availability

The data that support the findings of this study are available from Nanning Rail Transit Co., Ltd., but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of Nanning Rail Transit Co., Ltd.

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Acknowledgments

The research was supported by National Natural Science Foundation of China [Grant No. U22A2053], Major Project of Science and Technology of Guangxi Province of China [Grant No. Guike AA20302010], Interdisciplinary Scientific Research Foundation of Guangxi University [Grant No. 2022JCA003], and Innovation Project of Guangxi Graduate Education [Grant No. YCBZ2022043].

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Authors and Affiliations

  1. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Mechanical Engineering of Guangxi University, Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Nanning, 530004, China

    Jinxin Wu, Xianwang Li, Deqiang He & Qin Li

  2. Nanning Rail Transit Co., Ltd., Nanning, 530029, China

    Weibin Xiang

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  1. Jinxin Wu
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Correspondence to Deqiang He.

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Wu, J., Li, X., He, D. et al. Learning spatial-temporal dynamics and interactivity for short-term passenger flow prediction in urban rail transit. Appl Intell 53, 19785–19806 (2023). https://doi.org/10.1007/s10489-023-04508-5

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