DOI: 10.25338/b89h0f
title: National impacts of e-commerce growth: Development of a spatial demand based tool

description: 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.
description: The tasks of this project employ different combinations of
 methods to enable the prediction of e-commerce shopping behaviors for each
 MSA of interest at the individual level as well as the quantitative
 calculation of externalities. Methods used include Weighted Multinomial
 Logit (WMNL) models, time series forecasting, and Monte Carlo (MC)
 simulations, which are utilized throughout Task 1 to Task 5. 
 In Task 1, we mainly build and validate the WMNL behavior models
 for different MSAs with specific sets of model coefficients that can be
 used to predict shopping behavior for a synthesized population. In the
 WMNL mode, the dependent variable with totally four categories, namely “No
 shopping”, “In-store shopping”, “Online shopping” and “Both shopping”. The
 results of the WMNL models vary across MSAs, as reflected by the fact that
 different coefficients of variables are positive in some MSAs and negative
 in others. In general, however, female, high education, low to moderate
 age group, and not in labor market are the positive influences that make
 the respondents choose the online and/or both shopping. Four different
 population growth scenarios are specified with the combinations of
 high/moderate IV market share time series prediction and projected
 population. Also, the models are validated by the synthesized populations
 for the planning years, resulting in around 2% in the errors of dependent
 variable market share predictions. Tasks 2, 3 and 4
 provide information that supports the generation of shopping travels and
 the calculation of a series of negative externalities in Task 5 using
 Monte Carlo simulation, which is shopping travel parameters, last-mile
 delivery parameters and emission rate per person, respectively. 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, Task 5 is
 performed to serve the goal of the project: to simulate shopping behaviors
 for a synthesized population and to calculate related negative
 externalities. The MC simulation process is finalized by utilizing the
 results from Task 1 to 4, where the outputs of this part are the aggregate
 average VMT and emissions (negative externalities) for different shopping
 activities in the planning years and different MSA. This aggregate
 simulation results mainly come from calculations of VMT and emissions of
 the datasets of synthesized populations for different planning years and
 population growth scenarios. 
description: These data are from multiple sources in order to support the
 project titled “National Impacts of E-commerce Growth: Development of a
 Spatial Demand Based Tool”, funded by the National Center for Sustainable
 Transportation (NCST). The purpose of this project is to study the impacts
 of e-commerce on consumers’ shopping behaviors and the related
 externalities. Methods used include Weighted Multinomial Logit (WMNL)
 models, time series forecasting, and Monte Carlo (MC)
 simulations.  This project makes use of three primary
 datasets:  1. American Time Use Survey (ATUS)
 The project uses the 2004-2020 ATUS data to analyze shopping
 behaviors. The use of ATUS data is mainly for specifying shopping behavior
 models and extracting variables for the six chosen metropolitan
 areas. The ATUS data can be accessed at:
 https://timeuse.ipums.org/  2. National Household
 Travel Survey (NHTS) The project uses the 2009 and 2017
 NHTS data, which are based on trip-based surveys, to extract shopping
 travel parameters and last-mile delivery parameters for the six chosen
 metropolitan areas. Extracting shopping tours from NHTS requires
 identifying trip chains; we developed scripts to convert the raw
 trip-based data to tour-based data (refer to the code related to this
 project). The NHTS data can be accessed at:
 https://nhts.ornl.gov/  3. population
 projections The population projection data are produced
 in five-year increments from 2020 through 2100. These files are provided
 in .csv format. This project uses the data from 2020 through
 2050.  The projections are provided as totals, or
 segmented by age category (in four year increments), race/ethnicity and
 sex.  Note that the population projections are publicly
 available at the following DOI:
 https://doi.org/10.17605/OSF.IO/9YNFC  4. Individual
 Income Tax ZIP Code Data The Individual Income Tax ZIP
 Code data show selected income and tax items classified by State, ZIP
 Code, and size of adjusted gross income. Data are based on individual
 income tax returns filed with the IRS.  The Tax ZIP
 Code data can be accessed at:
 https://www.irs.gov/statistics/soi-tax-stats-data-by-geographic-area  5. MOVES Emission Rates The emission rates are compiled from EPA’s Motor Vehicle Emission Simulator (MOVES) model for the six chosen metropolitan areas and planning years (2020-2050).  The emission estimates are stored in MOVES_Emission_Rates.xlsx.  6. ZIP code level geographic data These data contain geographic boundaries for the six chosen metropolitan areas, as well as the socio-demographic data for each of the ZIP codes.  These data are stored in multiple files in different formats (specified below) in the folder geographic_data/.