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Impact of E-Bikes on Cycling in Hilly Areas: Participants’ Experience of Electrically-Assisted Cycling in a UK Study

Author

Listed:
  • Frauke Behrendt

    (Technology, Innovation and Society Group (TIS), Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, Atlas 5.402, P.O. Box 513, 5600 MB Eindhoven, The Netherlands)

  • Sally Cairns

    (Institute for Transport Studies, University of Leeds, 34-40 University Road, Leeds LS2 9JT, UK)

  • David Raffo

    (Department of Art and Design, University of Chester, Creative Campus, Kingsway, Chester CH2 2LB, UK)

  • Ian Philips

    (Institute for Transport Studies, University of Leeds, 34-40 University Road, Leeds LS2 9JT, UK)

Abstract

Electrically-assisted cycling can make a major contribution to sustainable mobility. For some people, hills are a barrier for cycling. This paper focuses on how and why electrically-assisted bikes make a difference to cycling in hilly contexts, and can thus be an important element of sustainable mobility in hilly geographies. It makes a novel contribution in being able to draw on recorded sensor data of the actual use of assistance in relation to the specific geography of the route cycled (GPS data), and interview/survey material. It shows that e-bikes extend the range, nature and scope of journeys manageable by bike, and therefore the general viability of e-bikes as alternative to other modes. It highlights that the benefits of using e-bikes are often also psychological, since they can change the overall enjoyability of the cycling experience in hilly areas. Resulting policy recommendations, that could lead to significant CO 2 savings through the uptake of e-cycling in hilly contexts, include ‘try before you buy’ schemes, training for e-bike users and investing in relevant cycling infrastructure.

Suggested Citation

  • Frauke Behrendt & Sally Cairns & David Raffo & Ian Philips, 2021. "Impact of E-Bikes on Cycling in Hilly Areas: Participants’ Experience of Electrically-Assisted Cycling in a UK Study," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8946-:d:611711
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    References listed on IDEAS

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    Cited by:

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    2. Maren Schnieder, 2023. "Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    3. Ovidiu R. Ciascai & Ștefan Dezsi & Karina A. Rus, 2022. "Cycling Tourism: A Literature Review to Assess Implications, Multiple Impacts, Vulnerabilities, and Future Perspectives," Sustainability, MDPI, vol. 14(15), pages 1-18, July.

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