Mapping the dynamics of electric vehicle charging demand within Beijing's spatial structure

Jing Kang, Hui Kong, Zhongjie Lin, Anrong Dang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Electric vehicles have been proliferating in large cities across the world, and we increasingly face challenges in estimating charging demand and in planning EV infrastructure. Focusing on Beijing as a case study, this research uses a novel data-driven method to measure the Charging Demand Indicators (CDI) derived from location-based service big data. Analyses through kernel density function reveal dynamic relations between the spatial patterns of CDI and the distribution of Public Charging Stations (PCS). Spatial match examination is conducted to discover areas of mismatch between charging demand and infrastructure supply. The results expose a CDI pattern which, although largely complies with the city's centripetal structure, demonstrates variations between weekdays and weekends and by EV travel distances. A spatial regression model confirms the influence of urban structure and distribution of amenities on EV charging behavior and suggests that particular land uses and location features have a significant association with EV charging demand. These findings shed light on the understanding of the spatial disparity between the CDI pattern and the current PCS distribution, which could inform future urban policies and planning of EV infrastructure with an emphasis on its coordination with land use, physical layout, and transit.

Original languageEnglish (US)
Article number103507
JournalSustainable Cities and Society
Volume76
DOIs
StatePublished - Jan 2022

Bibliographical note

Funding Information:
We owe a debt of gratitude to Baidu for providing data of this research, and to Dr. Mark Alan Hughes and Dr. Erick Guerra of University of Pennsylvania and Dr. Jason Cao of University of Minnesota for their inputs to the early version of this paper. This work is supported by National Key R&D Program of China ( 2018YFB2100701 ), National Natural Science Foundation of China General Program (No. 51878428 ), University of Pennsylvania China Research & Engagement Fund, Kleinman Center for Energy Policy research fund, and the USDOT Tier 1 University Transportation Center “Cooperative Mobility for Competitive Megaregions” (CM 2 ) (USDOT Award No. 69A3551747135) .

Funding Information:
We owe a debt of gratitude to Baidu for providing data of this research, and to Dr. Mark Alan Hughes and Dr. Erick Guerra of University of Pennsylvania and Dr. Jason Cao of University of Minnesota for their inputs to the early version of this paper. This work is supported by National Key R&D Program of China (2018YFB2100701), National Natural Science Foundation of China General Program (No. 51878428), University of Pennsylvania China Research & Engagement Fund, Kleinman Center for Energy Policy research fund, and the USDOT Tier 1 University Transportation Center ?Cooperative Mobility for Competitive Megaregions? (CM2) (USDOT Award No. 69A3551747135).

Publisher Copyright:
© 2021

Keywords

  • Charging demand indicator
  • Electric vehicle
  • Geospatial big data
  • Public charging station
  • Spatial regression model

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