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Cited by 581

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data

Open access
Dong, Jing, Liu, Changzheng, Lin, Zhenhong·2014·Transportation Research Part C: Emerging Technologies

Abstract

This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.

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Crossref · 10.1016/j.trc.2013.11.001

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Metadata

Authors
Dong, J., Liu, C., & Lin, Z.
Venue
Transportation Research Part C: Emerging Technologies
Publisher
Elsevier BV
Published
2014-01
Volume
38
Pages
44-55
ISSN
0968-090X (ISSN-L)
OpenAlex
W2050974717
Cited by
581
References
1
FWCI
13.98
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data. https://app.cochat.ai/papers/10.1016/j.trc.2013.11.001
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Dong, J., Liu, C., & Lin, Z. (2014). Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2013.11.001

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.