National impacts of e-commerce growth: Development of a spatial demand based tool

This project aims to study the impacts of e-commerce on shopping behaviors
and related externalities. The objectives are divided into five major
tasks in this project. Methods used include Weighted Multinomial Logit
(WMNL) models, time series forecasting, and Monte Carlo (MC) simulations.
The American Time Use Survey (ATUS) and the National Household Travel
Survey (NHTS) databases are used for identifying the independent and
dependent variables for behavioral modeling. At the same time, we
collected all MSA population data from the U.S. Census Bureau and combined
the shares of each variable from ATUS to generate a synthesized
population, which serves as input into the MC simulation framework
together with the behavioral model. This simulation framework includes the
generation of shopping travel parameters and the calculation of negative
externalities. We do this to estimate e-commerce demand and impacts every
decade until 2050. The results and analyses provide information that
supports the generation of shopping travel and the estimations of a series
of negative externalities using MC simulation, which includes shopping
travel parameters, last-mile delivery parameters, and emission rate per
person. For different parameters, a unique probability distribution or a
regression relation is obtained for different MSAs, and this distribution
is fed into the subsequent MC simulation. Finally, we simulated shopping
behaviors for synthesized populations (until 2050) and estimated the
expected negative externalities. The MC simulation generates aggregate
average vehicle miles traveled (VMT) and emissions (negative
externalities) for different shopping activities in the planning years and
different MSAs.