Real‐Time Multi‐Train Trajectory Optimisation and Delay Recovery Using SH‐MPC Integrated With Genetic Algorithms

ABSTRACT This paper introduces a dynamic optimisation system that enhances the management of train delays within automatic train operation (ATO) systems, utilising an innovative integration of shrinking‐horizon model predictive control (SH‐MPC) with genetic algorithms (GA). This research focuses on optimising train trajectories to efficiently handle various delay scenarios, from temporary speed restrictions to significant halts, ensuring both energy efficiency and punctuality. The proposed SH‐MPC addresses diverse delay situations in real time, while the integration with GA overcomes the limitations of long horizon forecasting. The simulation of multiple trains on a real route demonstrates the robustness of the proposed system in adhering to scheduled timetables while reducing energy consumption.