IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v321y2025ics0360544225011430.html
   My bibliography  Save this article

Unraveling inter-driver and intra-driver uncertainty: An eco-driving evaluation and optimization method

Author

Listed:
  • Huang, Jianchang
  • Wang, Xin
  • Lin, Qinghai
  • Song, Guohua
  • Yu, Lei

Abstract

Driving behavior exhibits uncertainties both between drivers (inter-driver) and within individual drivers (intra-driver). The proportions and relationships of these variations, especially the boundaries of individual drivers' comfort zones and the operational constraints of manual driving behavior, are not well understood in eco-driving research. This study aims to develop an eco-driving behavior evaluation and optimization model that accounts for individual driving habit constraints. Driver behavior instability was characterized by analyzing the standard deviation of output power. Inter-driver behavioral uncertainty was assessed by analyzing operational data from different drivers, while intra-driver behavioral uncertainty was represented through data from the same driver across various trip conditions. Furthermore, an individualized stepwise optimization eco-driving model (ED-ISOM) for varying driving conditions, incorporating physiological and psychological factors, was developed to provide comprehensive feedback on driver behavior. Findings reveal that, within the speed range of 20–100 km/h, the ratio of intra-driver to inter-driver energy consumption uncertainty ranges from 0.228 to 0.558 and increases with speed. Moreover, drivers classified under the same type may exhibit diverse driving behaviors. A normal-type driver traveling at speeds of 30–35 km/h displays 16.3 % aggressive behavior and 26.6 % cautious behavior. The ED-ISOM reduces fuel consumption by 3.5 %–10.1 %.

Suggested Citation

  • Huang, Jianchang & Wang, Xin & Lin, Qinghai & Song, Guohua & Yu, Lei, 2025. "Unraveling inter-driver and intra-driver uncertainty: An eco-driving evaluation and optimization method," Energy, Elsevier, vol. 321(C).
  • Handle: RePEc:eee:energy:v:321:y:2025:i:c:s0360544225011430
    DOI: 10.1016/j.energy.2025.135501
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225011430
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.135501?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Li, Jie & Fotouhi, Abbas & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2024. "Review on eco-driving control for connected and automated vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Liu, Jinqiang & Wang, Chunyan & Zhao, Wanzhong, 2024. "An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections," Energy, Elsevier, vol. 294(C).
    3. Wang, Yong & Wu, Yuankai & Tang, Yingjuan & Li, Qin & He, Hongwen, 2023. "Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 332(C).
    4. Ortega-Cabezas, Pedro-Miguel & Colmenar-Santos, Antonio & Borge-Diez, David & Blanes-Peiró, Jorge-Juan, 2021. "Can eco-routing, eco-driving and eco-charging contribute to the European Green Deal? Case Study: The City of Alcalá de Henares (Madrid, Spain)," Energy, Elsevier, vol. 228(C).
    5. Li, Jie & Fotouhi, Abbas & Pan, Wenjun & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2023. "Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties," Energy, Elsevier, vol. 279(C).
    6. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part II: Theoretical analysis," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 199-216.
    7. Wang, Pangwei & Wang, Xindi & Ye, Rongsheng & Sun, Yuanzhe & Liu, Cheng & Zhang, Juan, 2024. "Eco-driving-based mixed vehicular platoon control model for successive signalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    8. Huang, Yuhan & Ng, Elvin C.Y. & Zhou, John L. & Surawski, Nic C. & Chan, Edward F.C. & Hong, Guang, 2018. "Eco-driving technology for sustainable road transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 596-609.
    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. Li, Yuan & Pan, Chaofeng & Wang, Jian & Li, Zhongxing & Liang, Jun & Cai, Chongyu, 2024. "Research on energy consumption optimization of predictive cruise control considering the state of the leading vehicle," Energy, Elsevier, vol. 308(C).
    2. Sun, Xiaosong & Lu, Yongjie & Zheng, Lufeng & Li, Haoyu & Zhang, Xiaoting & Yang, Qi, 2025. "A novel eco-driving strategy for heterogeneous vehicle platooning with risk prediction and deep reinforcement learning," Energy, Elsevier, vol. 314(C).
    3. Ding, Heng & Sun, Yuan & Wang, Liangwen & Zheng, Xiaoyan & Huang, Wenjuan & Lu, Xiaoshan, 2024. "Intersection eco-driving strategies under mixed traffic environment: An novel cooperation of traffic signal and vehicle trajectory planning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 655(C).
    4. Li, Jie & Liu, Yonggang & Cheng, Jun & Fotouhi, Abbas & Chen, Zheng, 2024. "Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement," Energy, Elsevier, vol. 310(C).
    5. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    6. Li, Jianqi & Yang, Hang & Cheng, Rongjun & Zheng, Pengjun & Wu, Bing, 2024. "A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    7. Li, Menglin & Yin, Long & Yan, Mei & Wu, Jingda & He, Hongwe & Jia, Chunchun, 2024. "Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions," Energy, Elsevier, vol. 304(C).
    8. Liu, Qingling & Xu, Xiaowen, 2024. "A platoon-based eco-driving control mechanism for low-density traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    9. Li, Jie & Fotouhi, Abbas & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2024. "Review on eco-driving control for connected and automated vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    10. Santos, Alberto & Maia, Pedro & Jacob, Rodrigo & Wei, Huang & Callegari, Camila & Oliveira Fiorini, Ana Carolina & Schaeffer, Roberto & Szklo, Alexandre, 2024. "Road conditions and driving patterns on fuel usage: Lessons from an emerging economy," Energy, Elsevier, vol. 295(C).
    11. Yang Wang & Alessandra Boggio-Marzet, 2018. "Evaluation of Eco-Driving Training for Fuel Efficiency and Emissions Reduction According to Road Type," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    12. Robaina, Margarita & Neves, Ana, 2021. "Complete decomposition analysis of CO2 emissions intensity in the transport sector in Europe," Research in Transportation Economics, Elsevier, vol. 90(C).
    13. Ding, Yanyan & Jian, Sisi & Yu, Lin, 2025. "How to reduce carbon emissions in the urban transportation systems through carbon markets? Balancing the monetary and environmental benefits," Applied Energy, Elsevier, vol. 377(PB).
    14. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    15. 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.
    16. Juan Francisco Coloma & Marta García & Gonzalo Fernández & Andrés Monzón, 2021. "Environmental Effects of Eco-Driving on Courier Delivery," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    17. Li, Jie & Fotouhi, Abbas & Pan, Wenjun & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2023. "Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties," Energy, Elsevier, vol. 279(C).
    18. Xin Liu & Guojing Shi & Changbo Yang & Enyong Xu & Yanmei Meng, 2024. "Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections," Energies, MDPI, vol. 17(23), pages 1-22, November.
    19. Mehdizadeh, Milad & Solbu, Gisle & Klöckner, Christian A. & Moe Skjølsvold, Tomas, 2024. "Navigating acceptance and controversy of transport policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 187(C).
    20. Ghaemi Asl, Mahdi & Nie, Pu-yan & Charkh, Cyrus, 2024. "Cycles-specific benefits of smart transport for sustainable investing: Global and regional perspectives with different ethical paradigms," Technological Forecasting and Social Change, Elsevier, vol. 208(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    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:energy:v:321:y:2025:i:c:s0360544225011430. 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.journals.elsevier.com/energy .

    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.