IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8645-d862874.html
   My bibliography  Save this article

Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions

Author

Listed:
  • Manuel Blanco-Castillo

    (Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

  • Adrián Fernández-Rodríguez

    (Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

  • Antonio Fernández-Cardador

    (Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

  • Asunción P. Cucala

    (Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

Abstract

Eco-driving is a keystone in energy reduction in railways and a fundamental tool to contribute to the Sustainable Development Goals in the transport sector. However, its results in real applications are subject to uncertainties such as climatological factors that are not considered in the train driving optimisation. This paper aims to develop an eco-driving model to design efficient driving commands considering the uncertainty of climatological conditions. This uncertainty in temperature, pressure, and wind is modelled by means of fuzzy numbers, and the optimisation problem is solved using a Genetic Algorithm with fuzzy parameters making use of an accurate railway simulator. It has been applied to a realistic Spanish high-speed railway scenario, proving the importance of considering the uncertainty of climatological parameters to adapt driving commands to them. The results obtained show that the energy savings expected without considering climatological factors account for 29.76%, but if they are considered, savings can rise up to 34.7% in summer conditions. With the proposed model, a variation in energy of 5.32% is obtained when summer and winter scenarios are compared while punctuality constraints are fulfiled. In conclusion, the model allows the operator to estimate better energy by obtaining optimised driving adapted to the climate.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8645-:d:862874
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8645/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/14/8645/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. 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.
    3. Wenliang Zhou & Wenzhuang Fan & Xiaorong You & Lianbo Deng, 2019. "Demand-Oriented Train Timetabling Integrated with Passenger Train-Booking Decisions," Sustainability, MDPI, vol. 11(18), pages 1-34, September.
    4. Ling, Ximan & Peng, Yang & Sun, Shilin & Li, Panpan & Wang, Pu, 2018. "Uncovering correlation between train delay and train exposure to bad weather," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1152-1159.
    5. Wenliang Zhou & Xiaorong You & Wenzhuang Fan, 2020. "A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network," Sustainability, MDPI, vol. 12(3), pages 1-34, February.
    6. Li Wang & Yong Qin & Jie Xu & Limin Jia, 2012. "A Fuzzy Optimization Model for High-Speed Railway Timetable Rescheduling," Discrete Dynamics in Nature and Society, Hindawi, vol. 2012, pages 1-22, December.
    7. Huang, Ping & Wen, Chao & Fu, Liping & Lessan, Javad & Jiang, Chaozhe & Peng, Qiyuan & Xu, Xinyue, 2020. "Modeling train operation as sequences: A study of delay prediction with operation and weather data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    8. Aleksandar Blagojević & Sandra Kasalica & Željko Stević & Goran Tričković & Vesna Pavelkić, 2021. "Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    9. Emilia Koper & Andrzej Kochan, 2020. "Testing the Smooth Driving of a Train Using a Neural Network," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    10. Yang, Lixing & Li, Keping & Gao, Ziyou & Li, Xiang, 2012. "Optimizing trains movement on a railway network," Omega, Elsevier, vol. 40(5), pages 619-633.
    11. Wei, Dali & Liu, Hongchao & Qin, Yong, 2015. "Modeling cascade dynamics of railway networks under inclement weather," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 80(C), pages 95-122.
    12. Xuesong Feng & Hanxiao Zhang & Yong Ding & Zhili Liu & Hongqin Peng & Bin Xu, 2013. "A Review Study on Traction Energy Saving of Rail Transport," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-9, September.
    13. Yang, Jiawen & Quan, Jige & Yan, Bin & He, Canfei, 2016. "Urban rail investment and transit-oriented development in Beijing: Can it reach a higher potential?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 140-150.
    14. Wenliang Zhou & Mehdi Oldache, 2021. "Integrated Optimization of Line Planning, Timetabling and Rolling Stock Allocation for Urban Railway Lines," Sustainability, MDPI, vol. 13(23), pages 1-32, November.
    15. 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.
    16. 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.
    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.
    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. Elżbieta Szaruga & Elżbieta Załoga & Arkadiusz Drewnowski & Paulina Dąbrosz-Drewnowska, 2023. "Convergence of Energy Intensity of the Export of Goods by Rail Transport: Linkages with the Spatial Integration and Economic Condition of Countries," Energies, MDPI, vol. 16(9), pages 1-24, April.
    2. Gonzalo Sánchez-Contreras & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2023. "A Two-Level Fuzzy Multi-Objective Design of ATO Driving Commands for Energy-Efficient Operation of Metropolitan Railway Lines," Sustainability, MDPI, vol. 15(12), pages 1-24, June.

    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. Gonzalo Sánchez-Contreras & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2023. "A Two-Level Fuzzy Multi-Objective Design of ATO Driving Commands for Energy-Efficient Operation of Metropolitan Railway Lines," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    2. 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.
    3. Alejandro Cunillera & Adrián Fernández-Rodríguez & Asunción P. Cucala & Antonio Fernández-Cardador & Maria Carmen Falvo, 2020. "Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies," Energies, MDPI, vol. 13(7), pages 1-24, April.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. Ning, Jingjie & Zhou, Yonghua & Long, Fengchu & Tao, Xin, 2018. "A synergistic energy-efficient planning approach for urban rail transit operations," Energy, Elsevier, vol. 151(C), pages 854-863.
    9. 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.
    10. 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.
    11. 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.
    12. Li, Jiajie & Bai, Yun & Chen, Yao & Yang, Lingling & Wang, Qian, 2022. "A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times," Energy, Elsevier, vol. 260(C).
    13. 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.
    14. 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).
    15. Agostinho Rocha & Armando Araújo & Adriano Carvalho & João Sepulveda, 2018. "A New Approach for Real Time Train Energy Efficiency Optimization," Energies, MDPI, vol. 11(10), pages 1-21, October.
    16. 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.
    17. 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.
    18. Wang, Jinghui & Rakha, Hesham A., 2017. "Electric train energy consumption modeling," Applied Energy, Elsevier, vol. 193(C), pages 346-355.
    19. Yin, Jiateng & Yang, Lixing & Tang, Tao & Gao, Ziyou & Ran, Bin, 2017. "Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 182-213.
    20. 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.

    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:jsusta:v:14:y:2022:i:14:p:8645-:d:862874. 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.