IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3354-d1631321.html
   My bibliography  Save this article

Eco-Driving Optimization with the Traffic Light Countdown Timer in Vehicle Navigation and Its Impact on Fuel Consumption

Author

Listed:
  • Zhen Di

    (Jiangxi Provincial Key Laboratory of Comprehensive Stereoscopic Traffic Information Perception and Fusion, East China Jiaotong University, Nanchang 330013, China
    School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China)

  • Shihui Zhang

    (School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China)

  • Ayijiang Babayi

    (School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China)

  • Yuhang Zhou

    (School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China)

  • Shenghu Zhang

    (School of Mathematics and Statistics, Jiangxi Normal University, Nanchang 330039, China)

Abstract

For most drivers of fuel-powered vehicles who do not have specialized eco-driving knowledge, simple and practical strategies are the most effective way to encourage eco-driving habits. By incorporating traffic light countdown timers from vehicle navigation systems, this paper develops a 0–1 integer linear programming (ILP) model to determine the optimal speed curve and further provide actionable, easy-to-implement eco-driving recommendations. Specifically, time is discretized into one-second intervals, with speed and acceleration also discretized. Pre-calculating instantaneous fuel consumption under various speed and acceleration combinations ensures the linearity of the objective function. For a specified road and a given time duration, the optimal speed profile problem for approaching intersections is transformed into a series of speed and acceleration selections. Through the analysis of multiple application scenarios, this study proposes practical and easily adoptable eco-driving strategies, which can effectively reduce vehicle fuel consumption, thereby contributing to the sustainable development of urban traffic.

Suggested Citation

  • Zhen Di & Shihui Zhang & Ayijiang Babayi & Yuhang Zhou & Shenghu Zhang, 2025. "Eco-Driving Optimization with the Traffic Light Countdown Timer in Vehicle Navigation and Its Impact on Fuel Consumption," Sustainability, MDPI, vol. 17(8), pages 1-14, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3354-:d:1631321
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3354/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/8/3354/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin, Rui & Wang, Peggy, 2022. "Intention to perform eco-driving and acceptance of eco-driving system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 444-459.
    2. Saboohi, Y. & Farzaneh, H., 2009. "Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption," Applied Energy, Elsevier, vol. 86(10), pages 1925-1932, October.
    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. Wojciech Adamski & Krzysztof Brzozowski & Jacek Nowakowski & Tomasz Praszkiewicz & Tomasz Knefel, 2021. "Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions," Energies, MDPI, vol. 14(23), pages 1-15, November.
    2. Alejandro G. Tuero & Laura Pozueco & Roberto García & Gabriel Díaz & Xabiel G. Pañeda & David Melendi & Abel Rionda & David Martínez, 2017. "Economic Impact of the Use of Inertia in an Urban Bus Company," Energies, MDPI, vol. 10(7), pages 1-17, July.
    3. Nie, Zifei & Farzaneh, Hooman, 2022. "Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-netw," Energy, Elsevier, vol. 241(C).
    4. Li, Yangyang & Duan, Xiongbo & Fu, Jianqin & Liu, Jingping & Wang, Shuqian & Dong, Hao & Xie, Yunkun, 2019. "Development of a method for on-board measurement of instant engine torque and fuel consumption rate based on direct signal measurement and RGF modelling under vehicle transient operating conditions," Energy, Elsevier, vol. 189(C).
    5. Santos, Georgina & Behrendt, Hannah & Teytelboym, Alexander, 2010. "Part II: Policy instruments for sustainable road transport," Research in Transportation Economics, Elsevier, vol. 28(1), pages 46-91.
    6. Hooman Farzaneh & Jose A. Puppim de Oliveira & Benjamin McLellan & Hideaki Ohgaki, 2019. "Towards a Low Emission Transport System: Evaluating the Public Health and Environmental Benefits," Energies, MDPI, vol. 12(19), pages 1-17, September.
    7. Bartolozzi, I. & Rizzi, F. & Frey, M., 2013. "Comparison between hydrogen and electric vehicles by life cycle assessment: A case study in Tuscany, Italy," Applied Energy, Elsevier, vol. 101(C), pages 103-111.
    8. Carvalho, Irene & Baier, Thomas & Simoes, Ricardo & Silva, Arlindo, 2012. "Reducing fuel consumption through modular vehicle architectures," Applied Energy, Elsevier, vol. 93(C), pages 556-563.
    9. Leqi Zhang & Guohua Song & Zeyu Zhang & Zhiqiang Zhai & Junshi Xu & Pengfei Fan & Yan Ding, 2025. "Can Eco-Driving Evaluation Cross Cities? Data Localization and Behavioral Heterogeneity from Beijing to Toronto," Sustainability, MDPI, vol. 17(9), pages 1-21, April.
    10. Duhr, Pol & Christodoulou, Grigorios & Balerna, Camillo & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher H., 2021. "Time-optimal gearshift and energy management strategies for a hybrid electric race car," Applied Energy, Elsevier, vol. 282(PA).
    11. Hao Chen & Hesham A. Rakha, 2020. "Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized Intersections," Energies, MDPI, vol. 13(10), pages 1-16, May.
    12. Alam, Md. Saniul & McNabola, Aonghus, 2014. "A critical review and assessment of Eco-Driving policy & technology: Benefits & limitations," Transport Policy, Elsevier, vol. 35(C), pages 42-49.
    13. Sahoo, Lalit Kumar & Bandyopadhyay, Santanu & Banerjee, Rangan, 2014. "Benchmarking energy consumption for dump trucks in mines," Applied Energy, Elsevier, vol. 113(C), pages 1382-1396.
    14. Watling, David P. & Connors, Richard D. & Chen, Haibo, 2023. "Fuel-optimal truck path and speed profile in dynamic conditions: An exact algorithm," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1456-1472.
    15. Achour, H. & Carton, J.G. & Olabi, A.G., 2011. "Estimating vehicle emissions from road transport, case study: Dublin City," Applied Energy, Elsevier, vol. 88(5), pages 1957-1964, May.
    16. Dragan Lazarević & Libor Švadlenka & Valentina Radojičić & Momčilo Dobrodolac, 2020. "New Express Delivery Service and Its Impact on CO 2 Emissions," Sustainability, MDPI, vol. 12(2), pages 1-29, January.
    17. Sudarmanto Budi Nugroho & Eric Zusman, 2015. "Estimating greenhouse gas (GHG) emissions from paratransit in Bandung, Indonesia: Reducing the transaction costs of generating conservative emissions baselines," Natural Resources Forum, Blackwell Publishing, vol. 39(1), pages 53-63, February.
    18. Rouhani, Omid M. & Oliver Gao, H., 2014. "An advanced traveler general information system for Fresno, California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 254-267.
    19. Wang, Jinghui & Rakha, Hesham A., 2016. "Fuel consumption model for conventional diesel buses," Applied Energy, Elsevier, vol. 170(C), pages 394-402.
    20. Sina, Naser & Nasiri, Sayyad & Karkhaneh, Vahid, 2015. "Effects of resistive loads and tire inflation pressure on tire power losses and CO2 emissions in real-world conditions," Applied Energy, Elsevier, vol. 157(C), pages 974-983.

    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:17:y:2025:i:8:p:3354-:d:1631321. 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.