Multiperiod-based timetable optimization for metro transit networks

Abstract

This paper tackles the train timetable optimization problem for metro transit networks (MTN) in order to enhance the performance of transfer synchronization between different rail lines. Train timetables of connecting lines are adjusted in such a way that train arrivals at transfer stations can be well synchronized. This study particularly focuses on the timetable optimization problem in the transitional period (from peak to off-peak hours or vice versa) during which train headway changes and passenger travel demand varies significantly. A mixed integer nonlinear programming model is proposed to generate an optimal train timetable and maximize the transfer synchronization events. Secondly, an efficient hybrid optimization algorithm based on the Particle Swarm Optimization and Simulated Annealing (PSO-SA) is designed to obtain near-optimal solutions in an efficient way. Meanwhile, in order to demonstrate the effectiveness of the proposed method, the results of numerical example solved by PSO-SA are compared with a branch-and-bound method and other heuristicalgorithms. Finally, a real-world case study based on the Beijing metro network and travel demand is conducted to validate the proposed timetabling model. Computational results demonstrate the effectiveness of adjusting train timetables and the applicability of the developed approach to real-world metro networks.