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.
翻译:文献中广泛讨论了千禧年是否比前几代人更不以自动为中心的问题。大多数现有研究使用回归模型,并假定所有因素都是线性增加的,有助于年轻成年人的驾驶行为。本研究采用非参数统计学习方法,即梯度推动决策树(GBDT),放松了这一假设。本研究利用美国2001年和2017年全国旅行调查,审查了生命周期、社会人口和居住因素对千年和Gen-X青年每日驾驶距离的非线性剂量反应影响。所有其他因素不变,千禧年青年每天预计的驾驶距离比Gen-X年要短。此外,居住和经济因素解释了大约50%的青年每日驾驶距离,而生命过程事件和人口构成的集体贡献约为33%。本研究还确定了制定有效土地使用政策的密度范围,以减少汽车旅行需求。