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Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

Author

Listed:
  • Ran Tu

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Junshi Xu

    (Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada)

  • Tiezhu Li

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Haibo Chen

    (Institute for Transport Studies (ITS), University of Leeds, Leeds LS2 9JT, UK)

Abstract

Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of current guidance systems, static and dynamic, distinguished by whether real-world driving records are used to generate behaviour guidance or not. We find that influencing factors, such as the content of suggestions, the display methods, and drivers’ socio-demographic characteristics, have varied effects on the guidance results across studies. Drivers are reported to have basic eco-driving knowledge, while the question of how to motivate the acceptance and practice of such behaviour, especially in the long term, is overlooked. Adaptive driving suggestions based on drivers’ individual habits can improve the effectiveness and acceptance while this field is under investigation. In-vehicle assistance presents potential safety issues, and visualized in-vehicle assistance is reported to be most distractive. Given existing studies focusing on the operational level, a common agreement on the guidance design and associated influencing factors has yet to be reached. Research on the systematic and tactical design of eco-driving guidance and in-vehicle interaction is advised.

Suggested Citation

  • Ran Tu & Junshi Xu & Tiezhu Li & Haibo Chen, 2022. "Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review," IJERPH, MDPI, vol. 19(12), pages 1-14, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7310-:d:838715
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    References listed on IDEAS

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    Cited by:

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    2. Weiqi Zhou & Nanchi Wu & Qingchao Liu & Chaofeng Pan & Long Chen, 2023. "Research on Ecological Driving Following Strategy Based on Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(18), pages 1-14, September.

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