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Quantifying the relative contribution of factors to household vehicle miles of travel

机译:量化因素对家用车辆行驶里程的相对贡献

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Household vehicle miles of travel (VMT) has been exhibiting a steady growth in post-recession years in the United States and has reached record levels in 2017. With transportation accounting for 27 percent of greenhouse gas emissions, planning professionals are increasingly seeking ways to curb vehicular travel to advance sustainable, vibrant, and healthy communities. Although there is considerable understanding of the various factors that influence household vehicular travel, there is little knowledge of their relative contribution to explaining variance in household VMT. This paper presents a holistic analysis to identify the relative contribution of socio-economic and demographic characteristics, built environment attributes, residential self-selection effects, and social and spatial dependency effects in explaining household VMT production. The modeling framework employs a simultaneous equations model of residential location (density) choice and household VMT generation. The analysis is performed using household travel survey data from the New York metropolitan region. Model results showed insignificant spatial dependency effects, with socio-demographic variables explaining 33 percent, density (as a key measure of built environment attributes) explaining 12 percent, and self-selection effects explaining 11 percent of the total variance in the logarithm of household VMT. The remaining 44 percent remains unexplained and attributable to omitted variables and unobserved idiosyncratic factors, calling for further research in this domain to better understand the relative contribution of various drivers of household VMT.
机译:在美国经济衰退后的几年中,家用车行驶里程(VMT)一直保持稳定增长,并在2017年达到创纪录的水平。由于交通运输占温室气体排放量的27%,规划专业人士越来越多地寻求遏制方法推动可持续,充满活力和健康的社区的车辆旅行。尽管人们对影响家庭车辆旅行的各种因素有相当的了解,但对于它们对解释家庭VMT差异的相对贡献知之甚少。本文提供了一个整体分析来确定社会经济和人口特征,建筑环境属性,住宅自我选择效应以及社会和空间依赖效应在解释家庭VMT生产中的相对贡献。该建模框架采用居住区(密度)选择和家庭VMT生成的联立方程模型。使用来自纽约大都市区的家庭旅行调查数据进行分析。模型结果显示微不足道的空间依赖性效应,其中社会人口统计变量解释了33%,密度(作为构建环境属性的关键指标)解释了12%,自选效应解释了家庭VMT对数的总方差11% 。其余的44%仍无法解释,并且归因于遗漏的变量和未观察到的特质因素,因此呼吁对此领域进行进一步研究,以更好地了解家庭VMT的各种驱动因素的相对贡献。

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