Abstract Using US county-level data for 2017, this paper adopts a data-driven approach to study the main factors influencing carpooling for home-to-work trips. The potential explanatory variables quantify demographic, situational and judgmental characteristics of the counties. Since the proportion of workers carpooling for their commute is not (spatially) randomly distributed at the county level, the inclusion of spatial effects improves considerably the model's fit. The Spatial Autoregressive models show that eight variables (four related to demographics, three to situational and one to judgmental) do the best job of explaining the rates of carpooling. A Spatial Quantile Autoregression is further applied as a flexible and interpretable method to address the fact that some of the leading variables have varying effects on counties with different levels of carpooling. For instance, our results suggest that the agglomeration effect, measured by an increase in population density, has a gradual change in trend pattern because it encourages ridesharing among counties with low levels of carpooling, whereas it deters shared trips in high intensity carpooling areas. Alternatively, a decrease in car ownership, a variable strongly associated to counties' income, will lead to the largest increase in employees using carpooling for their home-to-work travels, and this relationship do not vary across quantiles.

中文翻译：

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拼车上班：美国县级的决定因素

摘要 本文利用2017年美国县级数据，采用数据驱动的方法研究影响家庭到工作拼车出行的主要因素。潜在的解释变量量化了县的人口统计、情境和判断特征。由于拼车上下班的工人比例不是（空间上）随机分布在县级，空间效应的加入大大提高了模型的拟合度。空间自回归模型表明，八个变量（四个与人口统计相关，三个与情境相关，一个与判断相关）最能解释拼车率。进一步应用空间分位数自回归作为一种灵活且可解释的方法，以解决一些主要变量对拼车水平不同的县有不同影响的事实。例如，我们的结果表明，以人口密度的增加衡量的集聚效应在趋势模式中具有逐渐变化的趋势，因为它鼓励拼车水平低的县之间的拼车，而它阻止了拼车强度高的地区的拼车。或者，汽车拥有量的减少（一个与县收入密切相关的变量）将导致使用拼车回家上班的员工人数增加最多，而且这种关系在分位数之间没有变化。以人口密度的增加来衡量，趋势模式逐渐发生变化，因为它鼓励拼车水平低的县之间的拼车，而阻止拼车强度高的地区的拼车。或者，汽车拥有量的减少（一个与县收入密切相关的变量）将导致使用拼车回家上班的员工人数增加最多，而且这种关系在分位数之间没有变化。以人口密度的增加来衡量，趋势模式逐渐发生变化，因为它鼓励拼车水平低的县之间的拼车，而阻止拼车强度高的地区的拼车。或者，汽车拥有量的减少（一个与县收入密切相关的变量）将导致使用拼车回家上班的员工人数增加最多，而且这种关系在分位数之间没有变化。