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Dominant charging location choice of commuters and non-commuters: a big data approach

Author

Listed:
  • Xiong Yang

    (The Hong Kong Polytechnic University)

  • Chengxiang Zhuge

    (The Hong Kong Polytechnic University
    The Hong Kong Polytechnic University
    The Hong Kong Polytechnic University Shenzhen Research Institute
    The Hong Kong Polytechnic University)

  • Chunfu Shao

    (Beijing Jiaotong University)

  • Runhang Guo

    (The Hong Kong Polytechnic University)

  • Andrew Tin Chak Wong

    (The Hong Kong Polytechnic University)

  • Xiaoyu Zhang

    (Beijing Jiaotong University)

  • Mingdong Sun

    (Beijing Jiaotong University)

  • Pinxi Wang

    (Beijing Transport Institute)

  • Shiqi Wang

    (The Hong Kong Polytechnic University)

Abstract

This paper is focused on electric vehicle (EV) users’ dominant charging locations, where they get their EVs recharged more frequently. We particularly compared the dominant charging location choice of commuters and non-commuters using a unique one-month trajectory dataset collected from 76,774 actual private EVs in Beijing in January 2018. Specifically, we first grouped EV users for both commuters and non-commuters according to their dominant charging locations and then characterized and compared their charging patterns. Further, we associated the dominant charging location choice of EV users with their characteristics using a mixed logistic regression model. The results suggested that over 50% of the EV users were the Home Dominated users with most charging events occurring around home. Further, there were significant differences in charging patterns of EV users from different groups by dominant charging location, and also between commuters and non-commuters. Commuters tended to have a lower SOC than non-commuters when they got their EVs recharged. Moreover, the dominant charging location choice of EV users was significantly associated with their characteristics, including charging opportunities available and mobility patterns, and the association is different for commuters and non-commuters. The results are expected to be useful for deploying charging infrastructure.

Suggested Citation

  • Xiong Yang & Chengxiang Zhuge & Chunfu Shao & Runhang Guo & Andrew Tin Chak Wong & Xiaoyu Zhang & Mingdong Sun & Pinxi Wang & Shiqi Wang, 2025. "Dominant charging location choice of commuters and non-commuters: a big data approach," Transportation, Springer, vol. 52(2), pages 439-466, April.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:2:d:10.1007_s11116-023-10427-8
    DOI: 10.1007/s11116-023-10427-8
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    References listed on IDEAS

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    1. Xiao-Hui Sun & Toshiyuki Yamamoto & Kazuhiro Takahashi & Takayuki Morikawa, 2018. "Home charge timing choice behaviors of plug-in hybrid electric vehicle users under a dynamic electricity pricing scheme," Transportation, Springer, vol. 45(6), pages 1849-1869, November.
    2. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    3. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    4. Gong, Huiming & Zou, Yuan & Yang, Qingkai & Fan, Jie & Sun, Fengchun & Goehlich, Dietmar, 2018. "Generation of a driving cycle for battery electric vehicles：A case study of Beijing," Energy, Elsevier, vol. 150(C), pages 901-912.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, Enero.
    6. Hess, Stephane & Polak, John W., 2005. "Mixed logit modelling of airport choice in multi-airport regions," Journal of Air Transport Management, Elsevier, vol. 11(2), pages 59-68.
    7. Wolbertus, Rick & Kroesen, Maarten & van den Hoed, Robert & Chorus, Caspar, 2018. "Fully charged: An empirical study into the factors that influence connection times at EV-charging stations," Energy Policy, Elsevier, vol. 123(C), pages 1-7.
    8. Yang Xu & Shih-Lung Shaw & Ziliang Zhao & Ling Yin & Zhixiang Fang & Qingquan Li, 2015. "Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach," Transportation, Springer, vol. 42(4), pages 625-646, July.
    9. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    10. Bolong Yun & Daniel (Jian) Sun & Yingjie Zhang & Siwen Deng & Jing Xiong, 2019. "A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
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