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A platform for research: civil engineering, architecture and urbanism
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.
Highlights Factors associated with U.S. young adult’s driving in 2001 and 2017 are examined. GBDT relax the assumption that factors contributing to driving are linear-additive. Holding other factors constant, young Millennials drove less than young Gen Xers. Life-cycle, ICT and non-motorized travel account for 23% of variations in driving.
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.
Highlights Factors associated with U.S. young adult’s driving in 2001 and 2017 are examined. GBDT relax the assumption that factors contributing to driving are linear-additive. Holding other factors constant, young Millennials drove less than young Gen Xers. Life-cycle, ICT and non-motorized travel account for 23% of variations in driving.
Generational differences in automobility: Comparing America's Millennials and Gen Xers using gradient boosting decision trees
Wang, Kailai (author) / Wang, Xize (author)
Cities ; 114
2021-03-24
Article (Journal)
Electronic Resource
English
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