Passenger flow analysis and emergency response simulation in a metro network using virus transmission model

OBJECTIVES: The potential virus in transportation facilities poses a serious risk to travelers. This research focus on the commuting by metro on the risk of the coronavirus disease 2019 (COVID-19). The main purpose is to explore the trajectory of virus transmission and the effectiveness of various control measures. METHODS: A transmission model was established on the basis of the susceptible-infected-recovered (SIR) model, combined with the spatial and temporal characteristics of the metro passenger flow. The implementation effects of the emergency strategies were analyzed through a series of simulation experiments. The changes in passenger flow affected by the virus transmission were analyzed both under the single intervention condition of the disinfection or off-peak travel policy and their double interventions. RESULTS: The results of the experiments show that disinfection and off-peak travel can effectively reduce the number of the infected people. To promote the disinfection is better than the off-peak travel strategy. The optimal solution is the combination of these two strategies, thereby reducing the infection rate in the stations effectively. In particular, it can reduce the number of potential infected people in high-traffic stations by 50%. CONCLUSIONS: This study provides a scientific basis for the prevention of COVID-19 in the urban transportation system and the formulation of public emergency strategies. It can also be applied to other epidemic diseases such as the seasonal flu, for public health prevention.