IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v52y2025i1p26-43.html

A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning

Author

Listed:
  • Bailing Zhang
  • Jing Kang
  • Tao Feng

Abstract

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Suggested Citation

  • Bailing Zhang & Jing Kang & Tao Feng, 2025. "A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning," Environment and Planning B, , vol. 52(1), pages 26-43, January.
    • Handle: RePEc:sae:envirb:v:52:y:2025:i:1:p:26-43
    DOI: 10.1177/23998083241249322
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/23998083241249322
    Download Restriction: no
    File URL: https://libkey.io/10.1177/23998083241249322?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Schroeder, Andreas & Traber, Thure, 2012. "The economics of fast charging infrastructure for electric vehicles," Energy Policy, Elsevier, vol. 43(C), pages 136-144.
    2. Zhi Cao & Jingbo Zhou & Meng Li & Jizhou Huang & Dejing Dou, 2023. "Urbanites’ mental health undermined by air pollution," Nature Sustainability, Nature, vol. 6(4), pages 470-478, April.
    3. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    4. Longxu Yan & De Wang & Shangwu Zhang & Dongcan Xie, 2019. "Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai," Transportation, Springer, vol. 46(3), pages 777-792, June.
    5. Jack N. Barkenbus, 2020. "Prospects for Electric Vehicles," Sustainability, MDPI, vol. 12(14), pages 1-13, July.
    6. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Micari, Salvatore & Polimeni, Antonio & Napoli, Giuseppe & Andaloro, Laura & Antonucci, Vincenzo, 2017. "Electric vehicle charging infrastructure planning in a road network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 98-108.
    2. Chen, Yu & Lin, Boqiang, 2022. "Are consumers in China’s major cities happy with charging infrastructure for electric vehicles?," Applied Energy, Elsevier, vol. 327(C).
    3. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    4. Bhardwaj, Chandan & Axsen, Jonn & Kern, Florian & McCollum, David, 2020. "Why have multiple climate policies for light-duty vehicles? Policy mix rationales, interactions and research gaps," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 309-326.
    5. Xu, Jiayi & Zhang, Xiao-Bing & Liu, Yang, 2024. "Asymmetric search behavior for gasoline prices: Evidence from the Chinese gasoline market," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 699-712.
    6. Savini, Federico & Kopp, Mira & Hochstenbach, Cody & Cohen, Leshem & Berrill, Peter & Merciai, Stefano, 2026. "Socio-ecological inequalities in housing consumption: How income, urban form, and tenure drive carbon footprints," Ecological Economics, Elsevier, vol. 242(C).
    7. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    8. Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.
    9. Motoaki, Yutaka & Yi, Wenqi & Salisbury, Shawn, 2018. "Empirical analysis of electric vehicle fast charging under cold temperatures," Energy Policy, Elsevier, vol. 122(C), pages 162-168.
    10. Mohamed Abdel-Basset & Abduallah Gamal & Ibrahim M. Hezam & Karam M. Sallam, 2024. "Sustainability assessment of optimal location of electric vehicle charge stations: a conceptual framework for green energy into smart cities," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 11475-11513, May.
    11. Neaimeh, Myriam & Salisbury, Shawn D. & Hill, Graeme A. & Blythe, Philip T. & Scoffield, Don R. & Francfort, James E., 2017. "Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles," Energy Policy, Elsevier, vol. 108(C), pages 474-486.
    12. Natascia Andrenacci & Antonino Genovese & Giancarlo Giuli, 2025. "Strategies for Workplace EV Charging Management," Energies, MDPI, vol. 18(2), pages 1-32, January.
    13. Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2020. "Location optimisation method for fast-charging stations along national roads," Journal of Transport Geography, Elsevier, vol. 88(C).
    14. Rahman, Imran & Vasant, Pandian M. & Singh, Balbir Singh Mahinder & Abdullah-Al-Wadud, M. & Adnan, Nadia, 2016. "Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1039-1047.
    15. Stergios Statharas & Yannis Moysoglou & Pelopidas Siskos & Pantelis Capros, 2021. "Simulating the Evolution of Business Models for Electricity Recharging Infrastructure Development by 2030: A Case Study for Greece," Energies, MDPI, vol. 14(9), pages 1-24, April.
    16. Xingang Zhou & Anthony G. O. Yeh, 2021. "Understanding the modifiable areal unit problem and identifying appropriate spatial unit in jobs–housing balance and employment self-containment using big data," Transportation, Springer, vol. 48(3), pages 1267-1283, June.
    17. Pichamon Keawthong & Veera Muangsin & Chupun Gowanit, 2022. "Location Selection of Charging Stations for Electric Taxis: A Bangkok Case," Sustainability, MDPI, vol. 14(17), pages 1-23, September.
    18. Asadi, Amin & Nurre Pinkley, Sarah, 2021. "A stochastic scheduling, allocation, and inventory replenishment problem for battery swap stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    19. Wang, Ying-Wei & Lin, Chuah-Chih, 2013. "Locating multiple types of recharging stations for battery-powered electric vehicle transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 58(C), pages 76-87.
    20. Kim, Hyunjung & Kim, Dae-Wook & Kim, Man-Keun, 2022. "Economics of charging infrastructure for electric vehicles in Korea," Energy Policy, Elsevier, vol. 164(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:52:y:2025:i:1:p:26-43. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.