Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China
Publication in refereed journal


Times Cited
Web of Science51WOS source URL (as at 25/10/2020) Click here for the latest count
Altmetrics Information
.

Other information
AbstractIn this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models - the set covering model, the maximal covering location model, and the p-median model - and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p-median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p-median and the maximal covering location models, our results suggest that (1) the p-median model outperforms the maximal covering location model in terms of satisfying the other's objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases. (C) 2016 Elsevier Ltd. All rights reserved.
All Author(s) ListHe SY, Kuo YH, Wu D
Journal nameTransportation Research Part C: Emerging Technologies
Year2016
Month6
Day1
Volume Number67
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Pages131 - 148
ISSN0968-090X
LanguagesEnglish-United Kingdom
KeywordsElectric vehicle; Facility location model; Maximal covering location model; Optimisation; p-median model; Set covering model
Web of Science Subject CategoriesTransportation; Transportation Science & Technology

Last updated on 2020-26-10 at 00:30