ABSTRACT
As one of the main means of transportation for citizens in Wuhan, urban rail transit has assumed the dual responsibility of ensuring the travel needs of citizens and blocking the spread of the epidemic in the context of COVID-19. Taking the security check space of Wuhan subway Street entrance station as an example, the paper aims at putting forward the optimization strategy of security space design to solve the obstruction problem caused by the excessive flow of subway stations at present. The paper takes the COVID-19 prevention and control requirements in Wuhan into consideration, uses intelligent technology, combines the construction of social force model to conduct pedestrian simulation, and applies simulation variable analysis. The findings indicate that the optimization strategy of security space design effectively shortens the arrival time and effectively controls the flow of people. It is expected to provide some reference and research basis for the design and optimization of security inspection space of subway transportation system in the future. © 2022 SPIE.
ABSTRACT
Epidemic prevention and control strongly affect people's lives in cities, but existing communicable disease models cannot accurately simulate the effects of prevention and control procedures. A city simulation model for the 2019 coronavirus epidemic was developed based on an Agent model for Wuhan, China to model the epidemic transmission process. The model includes the government control measures and the hospital diagnosis and treatment levels during the epidemic with analyses of the infection rates and spatial distributions for various epidemic control measures. The model was also used to model the active anti-epidemic impact of nucleic acid testing after people returned to work. The results show that this modeling method accurately reproduces the spatio-temporal transmission characteristics of the Wuhan epidemic. Thus, this method can be used to evaluate government control measures and to implement diagnosis and treatment plans for decision-making for infectious disease prevention and control. © 2021, Tsinghua University Press. All right reserved.