Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.

共享的按需移动服务在世界各地的城市中正在迅速扩展。举一个著名的例子，基于应用程序的拼车服务正在成为许多城市交通生态系统中不可或缺的一部分。尽管具有中心性，但有关乘车出行的详细时空数据的公众可用性有限，因此对新服务如何与传统出行方式相互作用以及它们如何影响城市出行的研究也很有限。改善数据共享协议正在为该领域的研究提供前所未有的机会。这项研究使用最近发布的芝加哥市公共出行数据研究了新兴的出行方式。详细的时空拼车数据与天气，过境和出租车数据相匹配，以更深入地了解拼车在芝加哥出行系统中的作用。目的是调查打车服务的系统性差异。由于K原型可以接受混合变量数据类型，因此可以利用它来检测用户细分。K-means算法的扩展，其输出是将数据分类为几个称为原型的簇。根据不利天气条件，与替代模式的竞争，使用地点和时间以及拼车倾向的重大差异，确定并讨论了六个拼车原型。本文讨论了已确定的集群与可负担性，公平性和过境竞争之间的关系。K-means算法的扩展，其输出是将数据分类为几个称为原型的簇。根据不利天气条件，与替代模式的竞争，使用地点和时间以及拼车倾向的重大差异，确定并讨论了六个拼车原型。本文讨论了已确定的集群与可负担性，公平性和过境竞争之间的关系。K-means算法的扩展，其输出是将数据分类为几个称为原型的簇。根据不利天气条件，与替代模式的竞争，使用地点和时间以及拼车倾向的重大差异，确定并讨论了六个拼车原型。本文讨论了已确定的集群与可负担性，公平性和过境竞争之间的关系。