IDEAS home Printed from https://ideas.repec.org/a/nat/natsus/v3y2020i6d10.1038_s41893-020-0533-6.html
   My bibliography  Save this article

Real-time data from mobile platforms to evaluate sustainable transportation infrastructure

Author

Listed:
  • Omar Isaac Asensio

    (Georgia Institute of Technology)

  • Kevin Alvarez

    (North Carolina State University)

  • Arielle Dror

    (Smith College)

  • Emerson Wenzel

    (Tufts University)

  • Catharina Hollauer

    (Georgia Institute of Technology)

  • Sooji Ha

    (Georgia Institute of Technology)

Abstract

By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine-learning based on 12,720 electric vehicle (EV) charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the rapidly expanding population of EV drivers in 651 core-based statistical areas in the United States. We deploy supervised machine-learning algorithms to automatically classify unstructured text reviews generated by EV users. Extracting behavioural insights at a population scale has been challenging given that streaming data can be costly to hand classify. Using computational approaches, we reduce processing times for research evaluation from weeks of human processing to just minutes of computation. Contrary to theoretical predictions, we find that stations at private charging locations do not outperform public charging locations provided by the government. Overall, nearly half of drivers who use mobility applications have faced negative experiences at EV charging stations in the early growth years of public charging infrastructure, a problem that needs to be fixed as the market for electrified and sustainable transportation expands.

Suggested Citation

  • Omar Isaac Asensio & Kevin Alvarez & Arielle Dror & Emerson Wenzel & Catharina Hollauer & Sooji Ha, 2020. "Real-time data from mobile platforms to evaluate sustainable transportation infrastructure," Nature Sustainability, Nature, vol. 3(6), pages 463-471, June.
  • Handle: RePEc:nat:natsus:v:3:y:2020:i:6:d:10.1038_s41893-020-0533-6
    DOI: 10.1038/s41893-020-0533-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41893-020-0533-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41893-020-0533-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunhan Zheng & David R. Keith & Shenhao Wang & Mi Diao & Jinhua Zhao, 2024. "Effects of electric vehicle charging stations on the economic vitality of local businesses," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Omar Isaac Asensio & Camila Z. Apablaza & M. Cade Lawson & Sarah Elizabeth Walsh, 2022. "A field experiment on workplace norms and electric vehicle charging etiquette," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 183-196, February.
    3. Omar Isaac Asensio & Camila Z. Apablaza & M. Cade Lawson & Edward W. Chen & Savannah J. Horner, 2022. "Impacts of micromobility on car displacement with evidence from a natural experiment and geofencing policy," Nature Energy, Nature, vol. 7(11), pages 1100-1108, November.
    4. Rocio de la Torre & Canan G. Corlu & Javier Faulin & Bhakti S. Onggo & Angel A. Juan, 2021. "Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications," Sustainability, MDPI, vol. 13(3), pages 1-21, February.
    5. Yang, Zaoli & Li, Qin & Yan, Yamin & Shang, Wen-Long & Ochieng, Washington, 2022. "Examining influence factors of Chinese electric vehicle market demand based on online reviews under moderating effect of subsidy policy," Applied Energy, Elsevier, vol. 326(C).
    6. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    7. Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).

    More about this item

    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:nat:natsus:v:3:y:2020:i:6:d:10.1038_s41893-020-0533-6. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.