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

TL;DR

This paper models household home delivery frequency during COVID-19 in Toronto, comparing econometric and machine learning methods to understand demand drivers and prediction accuracy.

Contribution

It develops and compares classical econometric and machine learning models to predict household delivery demand, highlighting socioeconomic and land use factors influencing e-shopping during the pandemic.

Findings

Ordered probit model accurately predicts aggregate demand distribution.

Machine learning models agree with econometric models on variable effects.

Econometric model outperforms machine learning in distribution prediction.

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.