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Who can drive electric? Segmentation of car drivers based on longitudinal GPS travel data

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  • Sodenkamp, Mariya
  • Wenig, Jürgen
  • Thiesse, Frédéric
  • Staake, Thorsten

Abstract

Current research on the driving performance and energy consumption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) considers several criteria, including electric reachability, load to the electric grid, and the share of distances that can be driven electrically. However, prior studies do not distinguish between different driver segments but treat the entirety of drivers as a coherent whole. These aggregated results are hence limited to a macro-level perspective or to an isolated assessment of the recruited driver sample, which reduces the validity of forecasts with respect to specific groups of adopters and their regional impact to the grid. In contrast, the present study outlines a procedure for a segment-wise analysis of drivers using GPS mobility data. The proposed approach allows for both, comparative usability analyses between distinct groups and predictions of the load to the electrical grid on a segment-by-segment basis. We illustrate our approach by the example of a dataset collected from 982 drivers in Italy over two years. The results support decision makers regarding the identification of segment-specific vehicle and infrastructure requirements. Moreover, the insights about benefits and obstacles of EV and PHEV adoption per segment help users take more informed purchasing decisions.

Suggested Citation

  • Sodenkamp, Mariya & Wenig, Jürgen & Thiesse, Frédéric & Staake, Thorsten, 2019. "Who can drive electric? Segmentation of car drivers based on longitudinal GPS travel data," Energy Policy, Elsevier, vol. 130(C), pages 111-129.
    • Handle: RePEc:eee:enepol:v:130:y:2019:i:c:p:111-129
    DOI: 10.1016/j.enpol.2019.03.048
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    References listed on IDEAS

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    Cited by:

    1. Powell, Siobhan & Cezar, Gustavo Vianna & Rajagopal, Ram, 2022. "Scalable probabilistic estimates of electric vehicle charging given observed driver behavior," Applied Energy, Elsevier, vol. 309(C).
    2. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).
    3. Manel Arribas-Ibar & Petra A. Nylund & Alexander Brem, 2021. "The Risk of Dissolution of Sustainable Innovation Ecosystems in Times of Crisis: The Electric Vehicle during the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(3), pages 1-14, January.
    4. Hauslbauer, Andrea L. & Schade, Jens & Petzoldt, Tibor, 2022. "The identification of mobility types on a national level," Transport Policy, Elsevier, vol. 125(C), pages 289-298.
    5. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).

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