  Despite the rapid growth of online shopping and research interest in the
relationship between online and in-store shopping, national-level modeling and
investigation of the demand for online shopping with a prediction focus remain
limited in the literature. This paper differs from prior work and leverages two
recent releases of the U.S. National Household Travel Survey (NHTS) data for
2009 and 2017 to develop machine learning (ML) models, specifically gradient
boosting machine (GBM), for predicting household-level online shopping
purchases. The NHTS data allow for not only conducting nationwide investigation
but also at the level of households, which is more appropriate than at the
individual level given the connected consumption and shopping needs of members
in a household. We follow a systematic procedure for model development
including employing Recursive Feature Elimination algorithm to select input
variables (features) in order to reduce the risk of model overfitting and
increase model explainability. Extensive post-modeling investigation is
conducted in a comparative manner between 2009 and 2017, including quantifying
the importance of each input variable in predicting online shopping demand, and
characterizing value-dependent relationships between demand and the input
variables. In doing so, two latest advances in machine learning techniques,
namely Shapley value-based feature importance and Accumulated Local Effects
plots, are adopted to overcome inherent drawbacks of the popular techniques in
current ML modeling. The modeling and investigation are performed both at the
national level and for three of the largest cities (New York, Los Angeles, and
Houston). The models developed and insights gained can be used for online
shopping-related freight demand generation and may also be considered for
evaluating the potential impact of relevant policies on online shopping demand.
