Generational differences in automobility: Comparing America's Millennials and Gen Xers using gradient boosting decision trees

《Generational differences in automobility: Comparing America's Millennials and Gen Xers using gradient boosting decision trees》

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关键字: Millennials;Life course events;Gradient boosting decision trees (GBDT);Driving distance;VMT;Machine learning

作者单位: Department of Construction Management, University of Houston, Houston, TX, USA;Department of Real Estate, National University of Singapore, Singapore;Department of Construction Management, University of Houston, Houston, TX, USA;Department of Real Estate, National University of Singapore, Singapore

摘要: 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.