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Location choice with longitudinal WiFi data

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  • Danalet, Antonin
  • Tinguely, Loïc
  • Lapparent, Matthieu de
  • Bierlaire, Michel

Abstract

Location-aware data collection technologies provide new insights about location choices. Only a few dynamic models of location choice exist in scientific literature. To our knowledge, none of them correct for serial correlation. In this paper, we model choice of catering locations on a campus using WiFi traces. We use the Wooldridge (2005) correction method that deals with the initial values problem and related endogeneity bias in estimation. Cross-validation, price elasticity and simulation of a scenario predicting the opening of a new catering location are presented. Predicted market shares of the new catering location correspond to point-of-sale data of the first week of opening.

Suggested Citation

  • Danalet, Antonin & Tinguely, Loïc & Lapparent, Matthieu de & Bierlaire, Michel, 2016. "Location choice with longitudinal WiFi data," Journal of choice modelling, Elsevier, vol. 18(C), pages 1-17.
    • Handle: RePEc:eee:eejocm:v:18:y:2016:i:c:p:1-17
    DOI: 10.1016/j.jocm.2016.04.003
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    Cited by:

    1. Ken Hidaka & Toshiyuki Yamamoto, 2021. "Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    2. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    3. Tosporn Arreeras & Mikiharu Arimura & Takumi Asada & Saharat Arreeras, 2019. "Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data," Sustainability, MDPI, vol. 11(14), pages 1-17, July.

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