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

Mingdong Sun (), Chunfu Shao (), Chengxiang Zhuge (), Pinxi Wang (), Xiong Yang () and Shiqi Wang ()
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Mingdong Sun: Beijing Jiaotong University
Chunfu Shao: Beijing Jiaotong University
Chengxiang Zhuge: The Hong Kong Polytechnic University
Pinxi Wang: Beijing Transport Institute
Xiong Yang: The Hong Kong Polytechnic University
Shiqi Wang: The Hong Kong Polytechnic University

Transportation, 2022, vol. 49, issue 5, No 6, 1409-1439

Abstract: 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).

Keywords: Electric vehicle; Trajectory data; Trip patterns; Charging patterns; Parking patterns; Spatiotemporal analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11116-021-10216-1

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