Abstract Emerging transportation services are quickly changing the way individuals travel by expanding the set of transportation alternatives available for a trip, allowing for more flexibility in travel schedules and providing access to transportation without incurring the costs of auto ownership. Among the most controversial and rapidly growing shared-mobility services are ridehailing services, such as those offered by Uber and Lyft in the U.S. market. In this paper, we investigate the factors affecting the adoption of ridehailing through the estimation of a latent-class adoption model that captures the heterogeneity in individuals’ tastes and preferences, focusing on members of the millennial generation and the preceding Generation X. We present a 3-class adoption model that provides better goodness of fit and interpretability of the classes compared to other model specifications that were tested. The three distinct classes have expected sample shares of 28%, 34% and 38%, respectively, and can be characterized as follows: (1) a class that is largely composed of more highly-educated, independent (i.e. who have already established their household) millennials, who has the highest adoption rate. Among other factors, the adoption of ridehailing services for the members of this class is influenced by the frequency of long-distance leisure and business-related trips made by non-car modes. The adoption of ridehailing among the members of this group is higher if they live in high-quality transit neighborhoods. (2) The second highest adoption rate is observed among the members of the class that is mainly composed of affluent individuals living with their families who are either dependent millennials or older members of Generation X. The frequency of use of transportation-related smartphone apps and the share of long-distance leisure trips made by plane affect the adoption of ridehailing for the members of this class. The members of this class also tend to adopt ridehailing if they live in neighborhoods with higher land-use mix and if they have used taxi services within the past 12 months. (3) Finally, the lowest adoption rate is observed among the members of the last class, comprising the least affluent individuals with the lowest level of education. The members of this class are more likely to live in rural neighborhoods and they rarely use ridehailing. The adoption of ridehailing among the members of this class is affected by household income, the frequency of long-distance non-car business trips, and transit accessibility as well as the use of taxi and of carsharing.

中文翻译：

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探索在加利福尼亚使用乘车服务背后的潜在构造

摘要新兴的运输服务通过扩大旅行可用的替代交通方式集，从而迅速改变了个人的出行方式，从而使旅行时间表更具灵活性，并提供了运输途径，而不会产生购置汽车的成本。最具争议性和快速增长的共享出行服务是叫车服务，例如Uber和Lyft在美国市场提供的服务。在本文中，我们通过评估潜在类别的采用模型来研究影响搭便车的因素，该模型捕获了个人口味和偏好的异质性，重点关注千禧一代和前X代的成员。我们提供了一个3类采用模型，与其他经过测试的模型规范相比，该模型提供了更好的拟合度和可解释性。这三个不同的类别的预期样本份额分别为28％，34％和38％，其特征如下：（1）该类别主要由受过高等教育的，独立的（即已经建立自己的家庭）千禧一代，其收养率最高。除其他因素外，此类乘车乘车服务的普及受到长途休闲的频率以及非汽车出行带来的商务旅行的影响。如果他们居住在高质量的过境社区中，那么他们中搭便车的比例会更高。（2）在该类成员中观察到第二高的采用率，该类成员主要是与家人生活在一起的富裕个人，他们既是千禧世代的千禧一代，又是X一代的老年成员。与交通有关的智能手机应用程序的使用频率和乘飞机进行长途休闲旅行的比例会影响到该类成员对搭便车的采用。如果此类成员居住在土地使用率较高的社区，并且在过去的12个月内使用出租车服务，他们也倾向于采用乘车游览。（3）最后，在最后一类成员中，收养率最低，包括那些受教育程度最低的富裕程度最低的人。此类成员更可能生活在农村地区，很少使用搭便车服务。