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http://hdl.handle.net/10397/116483
| Title: | Dominant charging location choice of commuters and non-commuters : a big data approach | Authors: | Yang, X Zhuge, C Shao, C Guo, R Wong, ATCR Zhang, X Sun, M Wang, P Wang, S |
Issue Date: | Apr-2025 | Source: | Transportation, Apr. 2025, v. 52, no. 2, p. 439-466 | 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. | Keywords: | Commuters Dominant charging location Electric vehicle Mixed logistic regression model Trajectory data |
Publisher: | Springer New York LLC | Journal: | Transportation | ISSN: | 0049-4488 | EISSN: | 1572-9435 | DOI: | 10.1007/s11116-023-10427-8 | Rights: | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023 This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11116-023-10427-8 |
| Appears in Collections: | Journal/Magazine Article |
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