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Passenger Flow Prediction of Scenic Spot Using a GCN–RNN Model

Author

Listed:
  • Zhijie Xu

    (School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China)

  • Liyan Hou

    (School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China)

  • Yueying Zhang

    (Artificial Intelligence College, Baoding University, Baoding 071000, China)

  • Jianqin Zhang

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China)

Abstract

The prediction and control of passenger flow in scenic spots is very important to the traffic management and safety of scenic spots. This study aims to predict the passenger flow of a scenic spot based on the passenger flow of the bus and subway stations around the scenic spots. We propose a passenger flow prediction model based on graph convolutional network–recurrent neural network (GCN–RNN). First, a “graph” is constructed according to the geographical relationship between the scenic spot and the surrounding bus and subway stations. Then, characteristics of surrounding areas of bus and subway stations are constructed based on the crowd behavior analysis, and these are then used as the node-information of the “graph”. Last, the GCN–RNN model is used to extract the temporal and spatial characteristics of the passenger flow data of the scenic spot to realize the prediction. The experimental results show that the proposed model is effective in passenger flow prediction in scenic spots.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3295-:d:769083
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    Citations

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    Cited by:

    1. 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.
    2. 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.

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