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Chinese Journal of Management Science ›› 2025, Vol. 33 ›› Issue (5): 236-246.doi: 10.16381/j.cnki.issn1003-207x.2022.1511

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Infection Risk Control of Pedestrians in Subway Stations under Epidemic of Respiratory Infectious Diseases

Jia Liu(), Xin Yuan, Weiqiao Ruan, Jinyu Bai   

  1. School of Information Engineering,Zhongnan University of Economics and Law,Wuhan 430073,China
  • Received:2022-07-10 Revised:2022-09-16 Online:2025-05-25 Published:2025-06-04
  • Contact: Jia Liu E-mail:liujia@zuel.edu.cn

Abstract:

In order to reduce the virus infection risk of pedestrians in the subway station, simulation modeling and optimization methods are used to study the management measures to reduce the infection rate of pedestrians in the station. A virus transmission model in subway stations is proposed by analyzing the spreading mechanism of respiratory infectious diseases virus and the probability of pedestrians being infected by the virus. A two-stage model of pedestrian movement in subway stations is proposed: massive video data is used to predict the target selection of pedestrians, the basic characteristics of pedestrian movement are extracted, and then the pedestrian route planning process is simulated. On this basis, a pedestrian motion simulation system in the subway station under the epidemic is established based on the NetLogo simulation platform. The SB-RS (Sequential Bifurcation-Response Surface) simulation optimization method is proposed that can be used for the study of pedestrian infection risk management in all double-deck Island subway stations under the epidemic. In the case analysis, suggestions on the optimal setting of pedestrian infection risk control in Wuhan Optics Valley Square station under the epidemic of respiratory infectious diseases are put forward, including the number of escalators, the running speed of escalators, the walking speed of pedestrians, and the number of entrances and exits.

Key words: subway station, COVID-19 epidemic, pedestrian movement model, virus transmission model, simulation optimization

CLC Number: