Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees

TL;DR

This study uses advanced machine learning to compare driving behaviors of Millennials and Gen Xers in the U.S., revealing non-linear effects of various factors and providing insights for land use policy.

Contribution

It introduces a non-parametric approach using gradient boosting decision trees to analyze generational differences in automobility, relaxing linear assumptions of prior studies.

Findings

Millennials have shorter daily driving distances than Gen Xers.

Residential and economic factors account for about 50% of driving distance variation.

Land use policies can target specific density ranges to reduce automobile travel demand.

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.