Integrated Optimization of Metro Train Timetabling and Passenger Flow Control with Skip–Stop Strategies under Respiratory Infectious Disease Outbreaks: A Simulation-Based Approach

Under the outbreaks of respiratory infectious disease, metro operators face significant challenges in effectively arranging train operations to meet passenger demand while minimizing infection risks. This paper proposes an integrated optimization model that simultaneously addresses passenger flow control and train timetabling with skip–stop strategies. The model accounts for train and station capacity constraints to ensure compliance with safe physical distancing requirements. Meanwhile, it considers station and platform types to accurately characterize passenger flow dynamics, thereby enhancing the model’s practicality. A genetic algorithm is designed to solve the model, incorporating a multiagent simulation method to demonstrate the dynamic time-space status of passenger and train movements and effectively verify the passenger flow control effect. Numerical experiments on Ningbo Metro Line 1 demonstrate that the proposed approach reduces total passenger travel time by 12%, with an 8% reduction in in-vehicle time and a 24% decrease in waiting time. Notably, the skip–stop strategy proves particularly effective in lowering in-vehicle time and balancing passenger distribution across stations, thereby mitigating the risk of disease transmission.