Volume 39 Issue 1
Feb.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(1): 111-117
MOU Zhenhua, LI Xiang, YAN Kangli, GUO Jijie. An Analysis of COVID-19 Propagation Model in Public Transportation Networks and Effectiveness of Epidemic Prevention Strategies[J]. Journal of Transport Information and Safety, 2021, 39(1): 111-117. doi: 10.3963/j.jssn.1674-4861.2021.01.0013
Citation: MOU Zhenhua, LI Xiang, YAN Kangli, GUO Jijie. An Analysis of COVID-19 Propagation Model in Public Transportation Networks and Effectiveness of Epidemic Prevention Strategies[J]. Journal of Transport Information and Safety, 2021, 39(1): 111-117. doi: 10.3963/j.jssn.1674-4861.2021.01.0013
shu

An Analysis of COVID-19 Propagation Model in Public Transportation Networks and Effectiveness of Epidemic Prevention Strategies

doi: 10.3963/j.jssn.1674-4861.2021.01.0013
  • 1.

    School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, Shandong, China

  • 2.

    Jinan Rail Transit Group Co., Ltd, Jinan 250101, Shandong, China

  • Received Date: 2020-09-05
  • Publish Date: 2021-02-28
    Abstract
  • Public transportation system is a critical potentiality space where airborne viruses have to spread between people. The study of the spread of viruses in the public transport system can accurately guide public transport epidemic prevention strategies. The two-layer public transportation network model, particle travel rules, and SEIR model are used to establish a public transportation network propagation model. Based on the background of virtual regional space and bus line network, characteristics of the two-layer network model are used to analyze the process of virus transmission on the bus and at the bus station. Both macro and micro epidemic prevention strategies are developed to analyze their effects. Public transportation causes the virus to spread on a large scale, and buses and bus stops are the most critical transmission links. For the public transportation epidemic prevention strategy, when the macro-control strategy cuts off the proportion of public transportation lines φ1 >0.5 or stops the proportion of public transportation stations φ2 >0.4, the final proportion of the immunized population will drop to below 0.3. The micro-adjustment strategy needs to control the departure interval td < 4 and the full load rate simultaneously α < 50%, so the final immune population ratio is less than 0.4, with the optimal prevention and control effect.

     

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