Robust MPC-Based Rescheduling for High-Density Intercity High-Speed Rail: A Focus on Train Headway Optimization

In this paper, we propose a robust model predictive control (MPC) approach for the rescheduling of intercity high-speed trains, considering the uncertainty of passenger flows. Intercity high-speed trains face challenges such as high speeds and frequent services, making rapid decision-making in scheduling crucial. This work focuses on the problem of restoring train headway, which offers greater flexibility, requires fewer adjustments, and improves passenger satisfaction. The proposed method involves developing a state-space model of an intercity high-speed rail loop that accounts for uncertain passenger flows and multiple train scenarios between stations. Subsequently, a corresponding robust MPC problem is formulated. Additionally, headway recovery and target capacity adjustments are designed and implemented based on the Lyapunov function and Cauchy inequality. The effectiveness of the proposed model and control method is validated through simulations on an intercity railway simulation platform. The simulation results demonstrate that the proposed approach effectively mitigates uncertain passenger flows and disturbances while meeting real-time operational requirements in practical scenarios.