Web: http://arxiv.org/abs/2209.10664

Sept. 23, 2022, 1:11 a.m. | Yicong Liu, Kaili Wang, Patrick Loa, Khandker Nurul Habib

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv covid covid-19 home modelling pandemics shopping travel

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