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Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines

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  • Wang, Pengling
  • Goverde, Rob M.P.

Abstract

This paper proposes a novel multi-train trajectory optimization for single-track lines. We restrict our attention to delay cases aiming at finding optimal speed profiles, which reduce delays and save energy consumption. A multi-train trajectory optimization method is proposed to find optimal meeting locations, arrival/departure times, and speed trajectories of multiple trains within the time and speed windows. The proposed method first finds timetable constraint sets for trains under delayed situations. The timetable constraint sets provide drivable, feasible, and energy-efficient time and speed windows along the trains’ routes. The multi-train trajectory optimization method uses minimizing energy consumption and reducing delays as the objective functions, and takes into account each train’s operational constraints as well as constraints to avoid conflicts between adjacent trains. Three driving strategies of delay-recovery, energy-efficient and on-time driving, are proposed and combined in the optimization objective selection for different delay scenarios. The multi-train trajectory optimization is formulated as a multiple-phase optimal control problem and solved by a pseudospectral method. The proposed method is applied in case studies of opposite trains running on a Dutch single-track railway corridor with different initial delay scenarios. The results show that our method is able to produce a feasible schedule with energy-efficient speed trajectories for all interacting trains.

Suggested Citation

  • Wang, Pengling & Goverde, Rob M.P., 2017. "Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 340-361.
  • Handle: RePEc:eee:transb:v:105:y:2017:i:c:p:340-361
    DOI: 10.1016/j.trb.2017.09.012
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    Cited by:

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    2. Zhang, Yongxiang & D'Ariano, Andrea & He, Bisheng & Peng, Qiyuan, 2019. "Microscopic optimization model and algorithm for integrating train timetabling and track maintenance task scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 237-278.
    3. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2018. "The two-train separation problem on non-level track—driving strategies that minimize total required tractive energy subject to prescribed section clearance times," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 135-167.
    4. Manuel Blanco-Castillo & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2022. "Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions," Sustainability, MDPI, vol. 14(14), pages 1-26, July.
    5. Artur Kierzkowski & Szymon Haładyn, 2022. "Method for Reconfiguring Train Schedules Taking into Account the Global Reduction of Railway Energy Consumption," Energies, MDPI, vol. 15(5), pages 1-18, March.
    6. Li, Wenxin & Peng, Qiyuan & Wen, Chao & Wang, Pengling & Lessan, Javad & Xu, Xinyue, 2020. "Joint optimization of delay-recovery and energy-saving in a metro system: A case study from China," Energy, Elsevier, vol. 202(C).
    7. Van Thielen, Sofie & Corman, Francesco & Vansteenwegen, Pieter, 2018. "Considering a dynamic impact zone for real-time railway traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 39-59.
    8. Ziyu Wu & Chunhai Gao & Tao Tang, 2021. "An Optimal Train Speed Profile Planning Method for Induction Motor Traction System," Energies, MDPI, vol. 14(16), pages 1-14, August.
    9. Szymon Haładyn, 2021. "The Problem of Train Scheduling in the Context of the Load on the Power Supply Infrastructure. A Case Study," Energies, MDPI, vol. 14(16), pages 1-19, August.
    10. Wang, Xuekai & Tang, Tao & Su, Shuai & Yin, Jiateng & Gao, Ziyou & Lv, Nan, 2021. "An integrated energy-efficient train operation approach based on the space-time-speed network methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    11. Zhan, Shuguang & Wang, Pengling & Wong, S.C. & Lo, S.M., 2022. "Energy-efficient high-speed train rescheduling during a major disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).

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