Modeling and Efficient Passenger-Oriented Control for Urban Rail Transit Networks

Real-time timetable scheduling is an effective way to improve passenger satisfaction and reduce operational costs in urban rail transit networks. In this paper, a novel passenger-oriented network model developed for real-time that can time-dependent origin-destination demands with consideration of balanced trade-off between accuracy computation speed. Then, predictive control (MPC) approach proposed the problem based on model. The resulting MPC optimization nonlinear non-convex problem. context, online computational complexity becomes main issue feasibility MPC. To complexity, therefore reformulated into mixed-integer linear programming (MILP) MILP exactly equivalent original be solved very efficiently by existing solvers, so we obtain solution fast realize scheduling. Numerical experiments part Beijing subway show effectiveness efficiency MILP-based method.