A Two-stage Method to Optimise Driving Strategy and Timetable for High-speed Trains

Abstract

Nowadays one of the major priorities for highspeed railways and operators is the reduction of energy consumption, considering the contradiction between limited resources and environmental pollution. Energy-efficient driving strategy and timetable optimization are two effective methods to minimize the energy consumption of high-speed railways. This paper combines driving strategy and timetable integrally to optimise train operation in successive sections. Primarily, the optimization model is established with the crucial objectives of energy consumption and trip time of each section. Then a two-stage approach is designed to solve the problem. First the quantum evolutionary algorithm (QEA) is implemented in order to find the optimal Pareto set of each section quickly and efficiently, and the corresponding Pareto curve can be obtained by fitting. In the second stage, the optimal trip time of each section and optimal operation strategy can be acquired based on internal penalty function (IPF). Finally, the algorithm is implemented in MATLAB with a case study on the regional train operating in four sections from Nanjing South station to Kunshan South station in China to verify the effectiveness of our proposed approaches.