Simulation analysis of epidemic prevention and regulation for metro passenger flow based on system dynamics

Abstract

According to the spreading mechanism of corona virus disease 2019 (COVID-19), the passengers in the metro system have a certain risk of being infected because the metro is a large capacity public transport mode with high passenger density in the stations. Relevant operational agencies need to organize passenger flow and take measures effectively to reduce infection risk in stations. In this paper, we establish a system dynamics model to simulate the passenger flow organization and distribution in metro stations. Then, we build an improved calculation model for the traffic-infected susceptibility and use it to predict the probability of infection. Four passenger flow control schemes are evaluated and their impacts on susceptibility are quantitatively analyzed. The four passenger flow control schemes include limiting the inbound passenger flow, controlling the number of service facilities, extending the streamline length of the station hall, and increasing the frequency of metro trains. The simulation results show that limiting the passenger flow is more effective in reducing the susceptibility of the unpaid area, increasing the frequency of metro trains is more effective in reducing the susceptibility of the paid area, and extending the streamline length of the station hall is more effective in reducing the susceptibility of the platform. We find that system dynamics modeling is an effective means to formulate and assess passenger flow organization schemes and control measures. The simulation results can provide a reference for metro station operational departments to take scientific epidemic prevention measures.