Ridesourcing services provided by companies like Uber, Lyft, and Didi have grown rapidly over the past decade and now serve a sizable portion of trips in many metropolitan areas. An understanding of these services (e.g. to whom, where, when, and for what purposes do they provide service?) is critical for regulating, planning, and managing urban multi-modal transportation systems effectively. Unfortunately, little is known about ridesourcing travel because private companies providing ridesourcing services were not previously subject to data sharing requirements. Fortunately, the city of Chicago recently collected and released spatially (census tract) and temporally (15-minute interval) aggregated data on ridesourcing trips collected from private companies. This study analyzes the Chicago ridesourcing data to examine factors influencing ridesourcing usage. The study employs a random-effects negative binomial (RENB) regression approach to model ridesourcing usage. Determinants considered in the model include weekend vs. weekday and weather variables as well as census tract socio-demographics and commute characteristics, land-use variables, places of interest, transit supply, parking features, and crime. The model results indicate ridesourcing demand is higher on days when temperatures are lower, there is less precipitation, and on the weekend, as well as in census tracts with (i) higher household incomes, (ii) a higher percentage of workers who carpool or take transit to work, (iii) a higher percentage of households with zero vehicles, (iv) higher population and employment density, (v) higher land-use diversity, (vi) fewer parking spots and higher parking rates, (vii) more restaurants, and (viii) more homicides. The results also demonstrate a non-linear (and insightful) relationship between ridesourcing demand and transit supply variables. The paper discusses the implications of these model results to inform transportation planning and policymaking as well as future research.

由Uber，Lyft和Didi等公司提供的Ridesourcing服务在过去十年中发展迅速，现在在许多大都市地区提供相当大的出行服务。对这些服务的了解（例如，它们向谁，何时何地以及为什么目的提供服务？）对于有效地调节，规划和管理城市多式联运系统至关重要。不幸的是，人们对骑行旅行并不了解，因为提供骑行服务的私人公司以前没有数据共享要求。幸运的是，芝加哥市最近收集并发布了从私人公司收集的关于乘车出行的空间（人口普查）和时间（间隔15分钟）汇总数据。这项研究分析了芝加哥的出行数据，以研究影响出行使用的因素。该研究采用随机效应负二项式（RENB）回归方法对乘车使用进行建模。该模型中考虑的决定因素包括周末与工作日，天气变量以及人口普查区的社会人口统计学和通勤特征，土地使用变量，景点，过境供应，停车位和犯罪。模型结果表明，在气温较低，降水较少的地方以及周末以及在（i）家庭收入较高，（ii）拼车或拼车的工人百分比较高的普查区中，出行需求较高。使过境工作，（iii）零车辆家庭的比例更高，（iv）人口和就业密度更高，（v）更高的土地利用多样性；（vi）更少的停车位和更高的停车率；（vii）更多的餐馆；以及（viii）更多的凶杀案。结果还证明了乘车出行需求和公交供应变量之间的非线性（且有见地）关系。本文讨论了这些模型结果对运输计划和政策制定以及未来研究的意义。