Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees
Abstract
Whether the Millennials are less auto-centric than the previous generations has been widely discussed in the literature. Most existing studies use regression models and assume that all factors are linear-additive in contributing to the young adults' driving behaviors. This study relaxes this assumption by applying a non-parametric statistical learning method, namely the gradient boosting decision trees (GBDT). Using U.S. nationwide travel surveys for 2001 and 2017, this study examines the non-linear dose-response effects of lifecycle, socio-demographic and residential factors on daily driving distances of Millennial and Gen-X young adults. Holding all other factors constant, Millennial young adults had shorter predicted daily driving distances than their Gen-X counterparts. Besides, residential and economic factors explain around 50% of young adults' daily driving distances, while the collective contributions for life course events and demographics are about 33%. This study also identifies the density ranges for formulating effective land use policies aiming at reducing automobile travel demand.
Keywords
Cite
@article{arxiv.2206.11056,
title = {Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees},
author = {Kailai Wang and Xize Wang},
journal= {arXiv preprint arXiv:2206.11056},
year = {2022}
}