Timetable Scheduling for Passenger-Centric Urban Rail Networks: Model Predictive Control based on a Novel Absorption Model

Timetable scheduling plays a key role in daily operations of urban rail transit systems, as it determines the quality of service provided to passengers. In order to develop efficient timetable scheduling methods, it is necessary to develop a proper model to integrate timetable-related and passenger-related factors in urban rail network efficiently. In this paper, a novel passenger absorption model for passenger- centric urban rail networks is established. The model explicitly integrates time-varying passenger origin-destination demands and the departure frequency of each line for real-time timetable scheduling. Then, a model predictive control (MPC) method for the timetable scheduling problem is proposed based on the developed model. The resulting MPC optimization problem can be formulated as a mixed-integer programming (MILP) problem, which can be solved efficiently by using the existing MILP solvers. The effectiveness of the absorption model and the corresponding MILP-based MPC approach is illustrated through the case study based on two Beijing subway lines.