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Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics

Author

Listed:
  • Yicong Liu
  • Kaili Wang
  • Patrick Loa
  • Khandker Nurul Habib

Abstract

The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict household' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.

Suggested Citation

  • Yicong Liu & Kaili Wang & Patrick Loa & Khandker Nurul Habib, 2022. "Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics," Papers 2209.10664, arXiv.org.
    • Handle: RePEc:arx:papers:2209.10664
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, October.
    2. Esra Suel & John W. Polak, 2018. "Incorporating online shopping into travel demand modelling: challenges, progress, and opportunities," Transport Reviews, Taylor & Francis Journals, vol. 38(5), pages 576-601, September.
    3. Qing Zhai & Xinyu Cao & Patricia L. Mokhtarian & Feng Zhen, 2017. "The interactions between e-shopping and store shopping in the shopping process for search goods and experience goods," Transportation, Springer, vol. 44(5), pages 885-904, September.
    4. Cao, Xinyu (Jason), 2012. "The relationships between e-shopping and store shopping in the shopping process of search goods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(7), pages 993-1002.
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