IDEAS home Printed from https://ideas.repec.org/a/eee/retrec/v54y2015icp41-50.html
   My bibliography  Save this article

Definition of energy-efficient speed profiles within rail traffic by means of supply design models

Author

Listed:
  • De Martinis, Valerio
  • Weidmann, Ulrich A.

Abstract

Nowadays, energy efficiency is a key requirement for railway systems in order to reduce operating costs. One of the main solutions for implementing energy efficiency is the optimization of train speed profiles to minimize tractive energy consumption. From a transportation systems point of view, the definition of optimized speed profiles should consider their possible impact on rail traffic flows, in order to evaluate their feasibility. To do so, an innovative optimization framework for the definition and the evaluation of energy efficient speed profiles, based on supply design modelling, is proposed. The framework operates on two levels: the first level generates energy efficient speed profiles with respect to timetable constraints, infrastructure characteristics and rolling stock features, and the second simulates these speed profiles on the rail network within the specific rail traffic conditions. Through this new integrated view, the evaluation of the proposed optimal speed profiles can fully take into account the operational requirements of the services, such as trains scheduling, absence of or small allowance for delays and respect for buffer times for passenger transfer at connecting stations. A numerical example based on a calibrated simulation model of the suburban S-Bahn S9 line that operates in the Canton of Zurich shows how energy efficient speed profiles can be defined considering the rail traffic influences, thus increasing the quality of the solutions.

Suggested Citation

  • De Martinis, Valerio & Weidmann, Ulrich A., 2015. "Definition of energy-efficient speed profiles within rail traffic by means of supply design models," Research in Transportation Economics, Elsevier, vol. 54(C), pages 41-50.
    • Handle: RePEc:eee:retrec:v:54:y:2015:i:c:p:41-50
    DOI: 10.1016/j.retrec.2015.10.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0739885915000682
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.retrec.2015.10.024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Phil Howlett, 2000. "The Optimal Control of a Train," Annals of Operations Research, Springer, vol. 98(1), pages 65-87, December.
    2. Ennio Cascetta, 2009. "Transportation Supply Models," Springer Optimization and Its Applications, in: Transportation Systems Analysis, chapter 0, pages 29-88, Springer.
    3. Liu, Rongfang (Rachel) & Golovitcher, Iakov M., 2003. "Energy-efficient operation of rail vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 917-932, December.
    4. Ennio Cascetta, 2009. "Transportation Supply Design Models," Springer Optimization and Its Applications, in: Transportation Systems Analysis, chapter 0, pages 589-620, Springer.
    5. Li, Xiang & Lo, Hong K., 2014. "Energy minimization in dynamic train scheduling and control for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 269-284.
    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. Pier Giuseppe Sessa & Valerio Martinis & Axel Bomhauer-Beins & Ulrich Alois Weidmann & Francesco Corman, 2021. "A hybrid stochastic approach for offline train trajectory reconstruction," Public Transport, Springer, vol. 13(3), pages 675-698, October.
    2. 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.
    3. Arkadiusz Kampczyk & Wojciech Gamon & Katarzyna Gawlak, 2023. "Integration of Traction Electricity Consumption Determinants with Route Geometry and Vehicle Characteristics," Energies, MDPI, vol. 16(6), pages 1-23, March.

    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. Canca, David & Zarzo, Alejandro, 2017. "Design of energy-Efficient timetables in two-way railway rapid transit lines," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 142-161.
    3. Ye, Hongbo & Liu, Ronghui, 2016. "A multiphase optimal control method for multi-train control and scheduling on railway lines," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 377-393.
    4. Gupta, Shuvomoy Das & Tobin, J. Kevin & Pavel, Lacra, 2016. "A two-step linear programming model for energy-efficient timetables in metro railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 57-74.
    5. 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.
    6. Scheepmaker, Gerben M. & Goverde, Rob M.P. & Kroon, Leo G., 2017. "Review of energy-efficient train control and timetabling," European Journal of Operational Research, Elsevier, vol. 257(2), pages 355-376.
    7. 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.
    8. 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.
    9. Zhaoxiang Tan & Shaofeng Lu & Kai Bao & Shaoning Zhang & Chaoxian Wu & Jie Yang & Fei Xue, 2018. "Adaptive Partial Train Speed Trajectory Optimization," Energies, MDPI, vol. 11(12), pages 1-33, November.
    10. Howlett, Phil, 2016. "A new look at the rate of change of energy consumption with respect to journey time on an optimal train journey," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 387-408.
    11. 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.
    12. Luan, Xiaojie & Wang, Yihui & De Schutter, Bart & Meng, Lingyun & Lodewijks, Gabriel & Corman, Francesco, 2018. "Integration of real-time traffic management and train control for rail networks - Part 2: Extensions towards energy-efficient train operations," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 72-94.
    13. Luijt, Ralph S. & van den Berge, Maarten P.F. & Willeboordse, Helen Y. & Hoogenraad, Jan H., 2017. "5years of Dutch eco-driving: Managing behavioural change," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 46-63.
    14. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 2: Existence of an optimal strategy, the local energy minimization principle, uniqueness, computational techniques," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 509-538.
    15. 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).
    16. Zhou, Leishan & Tong, Lu (Carol) & Chen, Junhua & Tang, Jinjin & Zhou, Xuesong, 2017. "Joint optimization of high-speed train timetables and speed profiles: A unified modeling approach using space-time-speed grid networks," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 157-181.
    17. Li, Xiang & Lo, Hong K., 2014. "An energy-efficient scheduling and speed control approach for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 73-89.
    18. Pier Giuseppe Sessa & Valerio Martinis & Axel Bomhauer-Beins & Ulrich Alois Weidmann & Francesco Corman, 2021. "A hybrid stochastic approach for offline train trajectory reconstruction," Public Transport, Springer, vol. 13(3), pages 675-698, October.
    19. Mariano Gallo & Marilisa Botte & Antonio Ruggiero & Luca D’Acierno, 2020. "A Simulation Approach for Optimising Energy-Efficient Driving Speed Profiles in Metro Lines," Energies, MDPI, vol. 13(22), pages 1-17, November.
    20. Wang, Chao & Meng, Xin & Guo, Mingxue & Li, Hao & Hou, Zhiqiang, 2022. "An integrated energy-efficient and transfer-accessible model for the last train timetabling problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).

    More about this item

    Keywords

    Railway systems; Energy efficiency; Supply design modelling;
    All these keywords.

    JEL classification:

    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation

    Statistics

    Access and download statistics

    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:eee:retrec:v:54:y:2015:i:c:p:41-50. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/620614/description#description .

    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.