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 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..., 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 paramete...