IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i4p436-d94344.html
   My bibliography  Save this article

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Author

Listed:
  • Jia Feng

    (School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
    Ministry of Education (MOE) Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Xiamiao Li

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

  • Haidong Liu

    (Ministry of Education (MOE) Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Xing Gao

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

  • Baohua Mao

    (Ministry of Education (MOE) Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

Abstract

How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach.

Suggested Citation

  • Jia Feng & Xiamiao Li & Haidong Liu & Xing Gao & Baohua Mao, 2017. "Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands," Energies, MDPI, vol. 10(4), pages 1-20, March.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:436-:d:94344
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/4/436/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/4/436/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheng Gong & Shiwen Zhang & Feng Zhang & Jianguo Jiang & Xinheng Wang, 2014. "An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One," Energies, MDPI, vol. 7(11), pages 1-25, November.
    2. Fei Lin & Shihui Liu & Zhihong Yang & Yingying Zhao & Zhongping Yang & Hu Sun, 2016. "Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm," Energies, MDPI, vol. 9(3), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luca D’Acierno & Marilisa Botte, 2018. "A Passenger-Oriented Optimization Model for Implementing Energy-Saving Strategies in Railway Contexts," Energies, MDPI, vol. 11(11), pages 1-25, October.
    2. 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).
    3. 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.
    4. Wafaa Saleh & Shekaina Justin & Ghada Alsawah & Tasneem Al Ghamdi & Maha M. A. Lashin, 2021. "Control Strategies for Energy Efficiency at PNU’s Metro System," Energies, MDPI, vol. 14(20), pages 1-13, October.
    5. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
    6. Pan, Deng & Zhao, Liting & Luo, Qing & Zhang, Chuansheng & Chen, Zejun, 2018. "Study on the performance improvement of urban rail transit system," Energy, Elsevier, vol. 161(C), pages 1154-1171.

    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. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 482-508.
    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. Fei Lin & Shihui Liu & Zhihong Yang & Yingying Zhao & Zhongping Yang & Hu Sun, 2016. "Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm," Energies, MDPI, vol. 9(3), pages 1-21, March.
    4. Andreas Bärmann & Alexander Martin & Oskar Schneider, 2017. "A comparison of performance metrics for balancing the power consumption of trains in a railway network by slight timetable adaptation," Public Transport, Springer, vol. 9(1), pages 95-113, July.
    5. Bing Bu & Guoying Qin & Ling Li & Guojie Li, 2018. "An Energy Efficient Train Dispatch and Control Integrated Method in Urban Rail Transit," Energies, MDPI, vol. 11(5), pages 1-23, May.
    6. Mo Chen & Zhuang Xiao & Pengfei Sun & Qingyuan Wang & Bo Jin & Xiaoyun Feng, 2019. "Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy," Energies, MDPI, vol. 12(18), pages 1-25, September.
    7. Shen, Xiaojun & Wei, Hongyang & Wei, Li, 2020. "Study of trackside photovoltaic power integration into the traction power system of suburban elevated urban rail transit line," Applied Energy, Elsevier, vol. 260(C).
    8. Guang Yang & Feng Zhang & Cheng Gong & Shiwen Zhang, 2019. "Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem," Energies, MDPI, vol. 12(18), pages 1-19, September.
    9. 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.
    10. Marcin Szott & Marcin Jarnut & Jacek Kaniewski & Łukasz Pilimon & Szymon Wermiński, 2021. "Fault-Tolerant Control in a Peak-Power Reduction System of a Traction Substation with Multi-String Battery Energy Storage System," Energies, MDPI, vol. 14(15), pages 1-23, July.
    11. Youneng Huang & Xiao Ma & Shuai Su & Tao Tang, 2015. "Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm," Energies, MDPI, vol. 8(12), pages 1-19, December.
    12. Zhang, Huan & Zhu, Chunguang & Zheng, Wandong & You, Shijun & Ye, Tianzhen & Xue, Peng, 2016. "Experimental and numerical investigation of braking energy on thermal environment of underground subway station in China's northern severe cold regions," Energy, Elsevier, vol. 116(P1), pages 880-893.
    13. 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.
    14. Janusz Szkopiński & Andrzej Kochan, 2021. "Energy Efficiency and Smooth Running of a Train on the Route While Approaching Another Train," Energies, MDPI, vol. 14(22), pages 1-27, November.
    15. Jie Yang & Limin Jia & Shaofeng Lu & Yunxiao Fu & Ji Ge, 2016. "Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution," Energies, MDPI, vol. 9(10), pages 1-27, September.
    16. Maria La Gennusa & Patrizia Ferrante & Barbara Lo Casto & Gianfranco Rizzo, 2015. "An Integrated Environmental Indicator for Urban Transportation Systems: Description and Application," Energies, MDPI, vol. 8(10), pages 1-19, October.
    17. Mulder, J. & van Jaarsveld, W.L. & Dekker, R., 2016. "Simultaneous optimization of speed and buffer times for robust transportation systems," Econometric Institute Research Papers EI2016-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Petru Valentin Radu & Adam Szelag & Marcin Steczek, 2019. "On-Board Energy Storage Devices with Supercapacitors for Metro Trains—Case Study Analysis of Application Effectiveness," Energies, MDPI, vol. 12(7), pages 1-22, April.
    19. Shuai Su & Tao Tang & Yihui Wang, 2016. "Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model," Energies, MDPI, vol. 9(2), pages 1-19, February.
    20. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2016. "A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 424-445.

    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:jeners:v:10:y:2017:i:4:p:436-:d:94344. 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.