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A Complete Information Interaction-Based Bus Passenger Flow Control Model for Epidemic Spread Prevention

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Listed:
  • Xinghua Hu

    (School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Yimei Xu

    (School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Jianpu Guo

    (Chongqing Productivity Council, Chongqing 401147, China)

  • Tingting Zhang

    (School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Yuhang Bi

    (Ningbo Citizen Card Operation Management Co., Ltd., Ningbo 315199, China)

  • Wei Liu

    (School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Xiaochuan Zhou

    (Chongqing Ulit Technology Co., Ltd., Chongqing 408319, China)

Abstract

Because the strategy of stopping bus lines during an epidemic can negatively impact residents, this study proposes a bus passenger flow control model to optimize the safety of and access to bus transport. The information interaction environment can provide a means for the two-way regulation of buses and passengers. In this model, passengers first request their pick-up and drop-off location, and then the bus feeds back information on whether it accepts the request. Through this method, passenger flow control can be realized through complete information interaction. The study aimed to establish a multi-objective function that minimizes the weighted total cost of the safety cost, the passenger travel cost, and the bus travel cost during an epidemic. The constraints were the full load and riding rates of urban buses in peak periods under the condition of epidemic prevention and control. The results showed that, in the morning peak period, the passenger flow control scheme reduced the passenger infection probability by 17.89%, compared with no passenger flow control scheme. The weighted total cost of the epidemic safety cost, the passenger travel cost, and the bus operation cost was reduced by 8.04%. The optimization effect of the passenger flow control scheme of this model is good, and not only reduces the probability of passengers being infected, but also meets the requirements of epidemic prevention and the travel needs of residents.

Suggested Citation

  • Xinghua Hu & Yimei Xu & Jianpu Guo & Tingting Zhang & Yuhang Bi & Wei Liu & Xiaochuan Zhou, 2022. "A Complete Information Interaction-Based Bus Passenger Flow Control Model for Epidemic Spread Prevention," Sustainability, MDPI, vol. 14(13), pages 1-11, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8032-:d:853117
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    References listed on IDEAS

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    1. Partohaghighi, Mohammad & Akgül, Ali, 2021. "Modelling and simulations of the SEIR and Blood Coagulation systems using Atangana-Baleanu-Caputo derivative," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    2. Kuo-Ying Wang, 2014. "How Change of Public Transportation Usage Reveals Fear of the SARS Virus in a City," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
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    Cited by:

    1. Pan Wu & Jinlong Li & Yuzhuang Pian & Xiaochen Li & Zilin Huang & Lunhui Xu & Guilin Li & Ruonan Li, 2022. "How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method," Sustainability, MDPI, vol. 14(15), pages 1-31, August.

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