IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i21p4491-d1270902.html
   My bibliography  Save this article

Pareto Optimization of Energy-Saving Timetables Considering the Non-Parallel Operation of Multiple Trains on a Metro Line

Author

Listed:
  • Weiya Chen

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
    Rail Data Research and Application Key Laboratory of Hunan Province, Changsha 410075, China)

  • Jiaqi Lu

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
    Rail Data Research and Application Key Laboratory of Hunan Province, Changsha 410075, China)

  • Hengpeng Zhang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Ziyue Yuan

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
    Rail Data Research and Application Key Laboratory of Hunan Province, Changsha 410075, China)

Abstract

In light of reducing train operation energy consumption while maintaining the passenger service level for creating sustainable urban rail transit systems, we address a non-parallel train timetabling problem considering regenerative braking energy utilization and the non-parallel operation of multiple trains on a metro line via a newly proposed multi-objective timetable (MOT) optimization model and an evolutionary algorithm based on NSGA-II. The optimization objectives of the MOT model are to find satisfactory energy-saving timetables on the Pareto frontier by minimizing the total travel time of passengers and minimizing the net energy consumption of trains. An improved multi-objective evolutionary algorithm based on NSGA-II is constructed to generate the optimal arrival and departure times at each station for each train running in a non-parallel operation mode. This study tests the feasibility of the proposed optimization method via an empirical case using the data collected from the Yizhuang Line of the Beijing metro systems in China. The simulation results show that the proposed optimization method satisfies both the energy utilization and passenger service levels along a Pareto front. The MOT improves the overall effectiveness of regenerative braking energy utilization by 29.88% in comparison with the original timetable; it reduces the net operation energy consumption by 44.86% relative to the travel-oriented timetable (TOT); and it reduces the total passenger travel time by 27.18% compared with the energy-oriented timetable (EOT).

Suggested Citation

  • Weiya Chen & Jiaqi Lu & Hengpeng Zhang & Ziyue Yuan, 2023. "Pareto Optimization of Energy-Saving Timetables Considering the Non-Parallel Operation of Multiple Trains on a Metro Line," Mathematics, MDPI, vol. 11(21), pages 1-19, October.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4491-:d:1270902
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/21/4491/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/21/4491/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Pengling & Goverde, Rob M.P., 2019. "Multi-train trajectory optimization for energy-efficient timetabling," European Journal of Operational Research, Elsevier, vol. 272(2), pages 621-635.
    2. Nakamura, Kazuki & Hayashi, Yoshitsugu, 2013. "Strategies and instruments for low-carbon urban transport: An international review on trends and effects," Transport Policy, Elsevier, vol. 29(C), pages 264-274.
    3. He, Deqiang & Yang, Yanjie & Chen, Yanjun & Deng, Jianxin & Shan, Sheng & Liu, Jianren & Li, Xianwang, 2020. "An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer," Applied Energy, Elsevier, vol. 264(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuan, Weichang & Frey, H. Christopher, 2020. "Potential for metro rail energy savings and emissions reduction via eco-driving," Applied Energy, Elsevier, vol. 268(C).
    2. Jiang Liu & Tian-tian Li & Bai-gen Cai & Jiao Zhang, 2020. "Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line," Energies, MDPI, vol. 13(8), pages 1-25, April.
    3. Selima Sultana & Hyojin Kim & Nastaran Pourebrahim & Firoozeh Karimi, 2018. "Geographical Assessment of Low-Carbon Transportation Modes: A Case Study from a Commuter University," Sustainability, MDPI, vol. 10(8), pages 1-23, August.
    4. 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.
    5. Yu Song & Guofan Shao & Xiaodong Song & Yong Liu & Lei Pan & Hong Ye, 2017. "The Relationships between Urban Form and Urban Commuting: An Empirical Study in China," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    6. Runsen Zhang & Tatsuya Hanaoka, 2022. "Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. AlSabbagh, Maha & Siu, Yim Ling & Guehnemann, Astrid & Barrett, John, 2017. "Integrated approach to the assessment of CO2e-mitigation measures for the road passenger transport sector in Bahrain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 203-215.
    8. Franciszek Restel & Łukasz Wolniewicz & Matea Mikulčić, 2021. "Method for Designing Robust and Energy Efficient Railway Schedules," Energies, MDPI, vol. 14(24), pages 1-12, December.
    9. 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).
    10. Zong, Fang & Li, Yu-Xuan & Zeng, Meng, 2023. "Developing a carbon emission charging scheme considering mobility as a service," Energy, Elsevier, vol. 267(C).
    11. Focas, Caralampo, 2016. "Travel behaviour and CO2 emissions in urban and exurban London and New York," Transport Policy, Elsevier, vol. 46(C), pages 82-91.
    12. Jing Gan & Linheng Li & Qiaojun Xiang & Bin Ran, 2020. "A Prediction Method of GHG Emissions for Urban Road Transportation Planning and Its Applications," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    13. Zhang, Linling & Long, Ruyin & Li, Wenbo & Wei, Jia, 2020. "Potential for reducing carbon emissions from urban traffic based on the carbon emission satisfaction: Case study in Shanghai," Journal of Transport Geography, Elsevier, vol. 85(C).
    14. Anna Górka & Andrzej Czerepicki & Tomasz Krukowicz, 2024. "The Impact of Priority in Coordinated Traffic Lights on Tram Energy Consumption," Energies, MDPI, vol. 17(2), pages 1-24, January.
    15. Andreas Bärmann & Alexander Martin & Oskar Schneider, 2021. "Efficient Formulations and Decomposition Approaches for Power Peak Reduction in Railway Traffic via Timetabling," Transportation Science, INFORMS, vol. 55(3), pages 747-767, May.
    16. Zhang, Lang & He, Deqiang & He, Yan & Liu, Bin & Chen, Yanjun & Shan, Sheng, 2022. "Real-time energy saving optimization method for urban rail transit train timetable under delay condition," Energy, Elsevier, vol. 258(C).
    17. Eliasson, Jonas & Proost, Stef, 2015. "Is sustainable transport policy sustainable?," Transport Policy, Elsevier, vol. 37(C), pages 92-100.
    18. He, Deqiang & Teng, Xiaoliang & Chen, Yanjun & Liu, Bin & Wang, Heliang & Li, Xianwang & Ma, Rui, 2022. "Energy saving in metro ventilation system based on multi-factor analysis and air characteristics of piston vent," Applied Energy, Elsevier, vol. 307(C).
    19. Xing, Zongyi & Zhang, Zhenyu & Guo, Jian & Qin, Yong & Jia, Limin, 2023. "Rail train operation energy-saving optimization based on improved brute-force search," Applied Energy, Elsevier, vol. 330(PA).
    20. Klauenberg, Jens & Elsner, Lucas-Andrés & Knischewski, Christian, 2020. "Dynamics of the spatial distribution of hubs in groupage networks – The case of Berlin," Journal of Transport Geography, Elsevier, vol. 88(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4491-:d:1270902. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.