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

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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.
Funding Information
  • National Key R&D Program of China (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • National Natural Science Foundation of China (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • Sichuan Science and Technology Program (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • Sichuan Science and Technology Program (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • Sichuan Science and Technology Program (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • Science and Technology Plan of China Railway Corporation (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • Chengdu soft science research project (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • Chengdu soft science research project (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • the Fundamental Research Funds for the Central Universities (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • the Fundamental Research Funds for the Central Universities (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • University College London (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • City, University of London (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)
  • University of Hong Kong (2017YFB1200702, 61703351, 2018RZ0078, 2019JDR0211, 2018123, P2018T001, 2017-RK00-00028-ZF, 2017-RK00-00378-ZF, 2682017CX022, 2682017CX018)

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