IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i8p1175-d1375460.html
   My bibliography  Save this article

SAD-ARGRU: A Metro Passenger Flow Prediction Model for Graph Residual Gated Recurrent Networks

Author

Listed:
  • Jilin Zhang

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Yanling Chen

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Shuaifeng Zhang

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Yang Zhang

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

Abstract

This paper proposes a graph residual gated recurrent network subway passenger flow prediction model considering the flat-peak characteristics, which firstly proposes the use of an adaptive density clustering method, which is capable of dynamically dividing the flat-peak time period of subway passenger flow. Secondly, this paper proposes graph residual gated recurrent network, which uses a graph convolutional network fused with a residual network and combined with a gated recurrent network, to simultaneously learn the temporal and spatial characteristics of passenger flow. Finally, this paper proposes to use the spatial attention mechanism to learn the spatial features around the subway stations, construct the spatial local feature components, and fully learn the spatial features around the stations to realize the local quantization of the spatial features around the subway stations. The experimental results show that the graph residual gated recurrent network considering the flat-peak characteristics can effectively improve the prediction performance of the model, and the method proposed in this paper has the highest prediction accuracy when compared with the traditional prediction model.

Suggested Citation

  • Jilin Zhang & Yanling Chen & Shuaifeng Zhang & Yang Zhang, 2024. "SAD-ARGRU: A Metro Passenger Flow Prediction Model for Graph Residual Gated Recurrent Networks," Mathematics, MDPI, vol. 12(8), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1175-:d:1375460
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/8/1175/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/8/1175/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhijie Xu & Liyan Hou & Yueying Zhang & Jianqin Zhang, 2022. "Passenger Flow Prediction of Scenic Spot Using a GCN–RNN Model," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Min Li & Mengshan Li & Bilong Liu & Jiang Liu & Zhen Liu & Dijia Luo, 2022. "Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention," Sustainability, MDPI, vol. 14(12), pages 1-17, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1175-:d:1375460. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.