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Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network

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  • Wang, Yaguan
  • Qin, Yong
  • Guo, Jianyuan
  • Cao, Zhiwei
  • Jia, Limin

Abstract

Prediction of passenger flow distribution in urban rail transit stations can provide important data support for passenger flow organization and passenger travel guidance. However, complex station space structure and simulation-based passenger flow data bring challenges to accurate analysis and prediction of the passenger flow inside the station. This paper proposes a temporal graph attention convolutional neural network model (TGACN) to predict the passenger flow volume and density in key areas of the station. Firstly, considering the topological structure of key areas and the characteristics of passenger flow and flow trend in the station, a multi-graph generation method for continuous space in stations is designed, including geographic neighborhood graph and semantic neighborhood graph, to represent the static and dynamic correlation between nodes. Secondly, a new method of spatio-temporal feature fusion is proposed, which takes multi-graph as input to optimize the extraction and expression of spatial and temporal correlation. Finally, the TGACN is verified by passenger flow data set, which is constructed based on real-time video monitoring data of a transit station in Guangzhou. Experiments demonstrate that the TGACN can obtain the spatio-temporal correlation from passenger flow data, and the prediction results are better than the existing baseline models.

Suggested Citation

  • Wang, Yaguan & Qin, Yong & Guo, Jianyuan & Cao, Zhiwei & Jia, Limin, 2022. "Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122006069
    DOI: 10.1016/j.physa.2022.127959
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    References listed on IDEAS

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    1. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    2. Wang, Jun & Wang, Wenjun & Liu, Xueli & Yu, Wei & Li, Xiaoming & Sun, Peiliang, 2022. "Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    3. Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
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    Citations

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    Cited by:

    1. Fenling Feng & Zhaohui Zou & Chengguang Liu & Qianran Zhou & Chang Liu, 2023. "Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    2. Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    3. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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