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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(3): 139-149
LEI Bin, LIU Xing-liang, CAO Zhen, HAO Ya-rui, ZHANG Yuan, CHEN Xin-miao. Modeling and forecasting of COVID-19 spread in urban rail transit system[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 139-149. doi: 10.19818/j.cnki.1671-1637.2020.03.013
Citation: LEI Bin, LIU Xing-liang, CAO Zhen, HAO Ya-rui, ZHANG Yuan, CHEN Xin-miao. Modeling and forecasting of COVID-19 spread in urban rail transit system[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 139-149. doi: 10.19818/j.cnki.1671-1637.2020.03.013
shu

Modeling and forecasting of COVID-19 spread in urban rail transit system

doi: 10.19818/j.cnki.1671-1637.2020.03.013
  • 1.

    School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China

  • 2.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2016YFC0802208

Shaanxi Transportation Technology Project 16-40K

Shaanxi Natural Science Basic Research Program 2017JQ5122

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    Abstract
  • Passenger group in urban rail transit system under the COVID-19 epidemic was divided into susceptible, infected and exposed ones considering urban rail transit travel characters. In the research environment of COVID-19 free spread and the case sampling time of early spread, the infection probability of 0.41 was selected based on related studies. The urban rail transit ridership in COVID-19 case was divided into inbound/outbound phase and riding phase. Considering COVID-19 effective spread range, passengers distribution and moving characters, the COVID-19 spreading model in the urban rail transit system was built. Taking the metro system in a certain city as the simulation case, 13 patients in metro ridership cases were assumed. With the accessibility of historical passenger data, the parameters in the model were determined. The possible infections in different loading levels were forecasted, and the elements related to possible infections were discussed. Analysis result indicates that when the loading level decreases to 10% of the average level, the possible infections in most cases are less than 1, which proves the effectiveness of current urban rail transit passenger control strength. The change in possible infections caused by the reduction in passenger number in the start/terminal(less than 20%) is less than that caused by the reduction in passenger number in the vehicle(60%-80%). Therefore, the passenger density in the vehicle has more significant impact on the possible infection compared to the passenger number in the start/terminal. When passing the stop, if the ratio of the on/off board passenger numbers is no more than 1, the possible infections can be controlled effectively. If making the possible infections nonlinearly and positively correlate to the stop number, the function among the loading level, stop number and possible infections will have better performance(determination coefficient is 0.700 1).

     

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