Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic

Subways play an important role in public transportation to and from work. In the traditional working system, the commuting time is often arranged at fixed time nodes, which directly leads to the gathering of "morning peak" and "evening peak" in the subway. Under the COVID-19 pandemic, this congestion is exacerbating the spread of the novel coronavirus. Several countries have resorted to the strategy of stopping production to curb the risk of the spread of the epidemic seriously affecting citizens' living needs and hindering economic operation. Therefore, orderly resumption of work and production without increasing the risk of the spread of the epidemic has become an urgent problem to be solved. To this end, we propose a mixed integer programming model that takes into account both the number of travelers and the efficiency of epidemic prevention and control. Under the condition that the working hours remain the same, it can adjust the working days and commuting time flexibly to realize orderly off-peak travel of the workers who return to work. Through independent design of travel time and reasonable control of the number of passengers, the model relaxes the limitation of the number of subway commuters and reduces the probability of cross-travel between different companies. We also take the data of Beijing subway operation and apply it to the solution of our model as an example. The example analysis results show that our model can realize the optimal travel scheme design of returning to work at the same time node and avoiding the risk of cross infection among enterprises under different epidemic prevention and control levels.