IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v47y2024i4p540-565.html
   My bibliography  Save this article

In-store shopping trip predictions and impact factors during COVID-19 emergencies

Author

Listed:
  • Md Ashraful Imran
  • Kate Hyun

Abstract

Despite the rapid growth of online shopping during COVID-19, a significant number of consumers still prefer in-store shopping. This study leverages two years (i.e. pre-pandemic and pandemic) of smartphone location data to develop machine learning (ML) models, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost), for predicting community (e.g. block group (BG)) level in-store shopping trips for department stores, shopping malls, supermarkets, and wholesale stores. This study identifies that temperature, accessibility to stores, and the number of online shopping last-mile delivery are the three most important factors influencing shopping trips; specifically, the extent of online shopping is a critical determinant for supermarkets and wholesale store trip-makings before and during the pandemic. The models developed and important determinants of shopping trips will provide useful insight for shopping trip demand forecasting as well as impact assessments of relevant policies on in-store shopping demand during emergencies.

Suggested Citation

  • Md Ashraful Imran & Kate Hyun, 2024. "In-store shopping trip predictions and impact factors during COVID-19 emergencies," Transportation Planning and Technology, Taylor & Francis Journals, vol. 47(4), pages 540-565, May.
    • Handle: RePEc:taf:transp:v:47:y:2024:i:4:p:540-565
    DOI: 10.1080/03081060.2023.2282059
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2023.2282059
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060.2023.2282059?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.

    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:taf:transp:v:47:y:2024:i:4:p:540-565. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    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.