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Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications

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Abstract

The market penetration rate of electric vehicle (EV) is on the rise globally. However, the use behaviors of private EVs have not been well understood, in part due to the lack of proper datasets. This paper used a unique dataset containing trajectories of over 76,000 private EVs (accounting for 68% of the private EV fleet) in Beijing to uncover trip, parking and charging patterns of private EVs, so as to better inform policy making and infrastructure planning for different EV-related stakeholders, including planners, vehicle manufacturers, and power grid and infrastructure companies. We conducted both statistical and spatiotemporal analyses. In terms of statistical patterns, most of the EV trip distances (over 71%) were shorter than 15 km. Also, most of parking events (around 76%) lasted for less than 1 h. From a spatial perspective, the densities of trip Origins and Destinations (ODs), parking events and charging events in the central districts tended to be much higher than those of the other districts. Furthermore, the number of intra-district trips tended to be much higher than the number of inter-district trips. In terms of temporal trip patterns, there were two peak periods on working days: a morning peak period from 7 to 9 AM, and an afternoon peak period from 5 to 7 PM; On non-working days, there was only one peak period from 9 AM to 5 PM; while the temporal charging patterns on working and non-working days had a similar trend: most of EV drivers got their EVs charged overnight. Finally, we demonstrated how to apply the observed statistical and spatiotemporal patterns into policy making (i.e., time-of-use tariff) and infrastructure planning (i.e., deployment of normal charging posts, enroute fast charging stations and vehicle-to-grid enabled infrastructures).

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Acknowledgements

This work was supported by the Hong Kong Polytechnic University [1-BE2J], and the National Natural Science Foundation of China (52002345; 52072025).

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Authors and Affiliations

  1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Xizhimenwai, Beijing, 100044, China

    Mingdong Sun & Chunfu Shao

  2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Chengxiang Zhuge, Xiong Yang & Shiqi Wang

  3. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China

    Chengxiang Zhuge

  4. Beijing Transport Institute, No. 9 LiuLiQiao South Lane, Fengtai District, Beijing, 100073, China

    Pinxi Wang

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  1. Mingdong Sun
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  2. Chunfu Shao
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Contributions

Mingdong Sun: Literature Review, Research Conception and Design, Computing, and Manuscript Writing and Editing; Chunfu Shao: Manuscript Writing and Editing and Research Conception and Design; Chengxiang Zhuge: Manuscript Writing and Editing and Research Conception and Design; Pinxi Wang: Computing, and Manuscript Writing and Editing; Xiong Yang: Computing, and Manuscript Writing and Editing; Shiqi Wang: Literature Review, and Manuscript Writing and Editing.

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Correspondence to Chengxiang Zhuge.

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Sun, M., Shao, C., Zhuge, C. et al. Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications. Transportation 49, 1409–1439 (2022). https://doi.org/10.1007/s11116-021-10216-1

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