Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving

Abstract

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes.

Train Scheduling Optimization for an Urban Rail Transit Line: A Simulated-Annealing Algorithm Using a Large Neighborhood Search Metaheuristic

“…We now investigate the average waiting time and average travel time of passengers under the optimized schedule. Te average waiting time T ave_wait and average travel time T ave_tra are calculated according to equations (34) and (35), respectively, where T wait is the total waiting time of all passengers, T tra_train is the total travel time of passengers within the train, and N p is the total number of passengers. As shown in equation (36), the total waiting time of passengers T wait consists of the waiting time of passengers boarding the current train and the waiting time of passengers who remain on the platform.…”

“…Barrena et al [32] proposed two nonlinear programming formulations for optimizing a rapid-transit timetable under dynamic PTD, with the objective of minimizing the average passenger waiting time at stations. Canca et al [33] presented a nonlinear integer-programming model for determining noncyclic railway timetables under dynamic PTD, while Chen et al [34] presented an energy-saving timetable optimization model that minimizes the energy consumption of train stops and the travel time of trains. Meng et al [35] presented an optimization model and heuristic technique for reallocating the time margins in train timetables so as to minimize train delays under operational disturbances.…”

A Green Transportation Planning Approach for Coal Heavy-Haul Railway System by Simultaneously Optimizing Energy Consumption and Capacity Utilization

“…Cucala [19] Maria [20] Energy optimization schedule, energy-efficient driving strategies Integrated optimization of train schedules and driver behavior Xiang L. [21] Driver behavior analysis Dynamic train control and driving solution optimization Dingjun Chen [22] Bi-objective evolutionary algorithm Establishing an energy-efficient operation diagram for high-speed railways based on stop and dispatch optimization Table 1. Cont.…”