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 impact travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examines emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data are matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcings role in Chicagos mobility system. The goal is to investigate the systematic variations in patronage of ride-hailing. K-prototypes is utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output is a classification of the data into several clusters called prototypes. Six ridesourcing prototypes are 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 implications of the identified clusters related to affordability, equity and competition with transit.

中文翻译：

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大规模拼车出行数据的K-Prototype分割分析

共享出行按需服务正在世界各地的城市迅速扩张。作为一个突出的例子，基于应用程序的叫车服务正在成为许多城市交通生态系统不可或缺的一部分。尽管处于中心地位，但关于拼车旅行的详细时间和空间数据的公共可用性有限，这限制了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旅行的研究。改进数据共享协议为该领域的研究开辟了前所未有的机会。本研究使用最近发布的芝加哥市公共乘车数据来研究新兴的出行模式。详细的时空拼车数据与天气、交通和出租车数据相匹配，以更深入地了解拼车在芝加哥移动系统中的作用。目标是调查叫车服务的系统变化。由于 K-prototypes 能够接受混合变量数据类型，因此它被用于检测用户细分。K-means 算法的扩展，其输出是将数据分类为多个称为原型的集群。根据与不利天气条件、与替代模式的竞争、使用位置和时间以及拼车趋势等方面的显着差异，确定并讨论了六个拼车原型。本文讨论了与可负担性、公平性和交通竞争相关的已识别集群的影响。K-means 算法的扩展，其输出是将数据分类为多个称为原型的集群。根据与不利天气条件、与替代模式的竞争、使用位置和时间以及拼车趋势等方面的显着差异，确定并讨论了六个拼车原型。本文讨论了与可负担性、公平性和交通竞争相关的已识别集群的影响。K-means 算法的扩展，其输出是将数据分类为多个称为原型的集群。根据与不利天气条件、与替代模式的竞争、使用位置和时间以及拼车趋势等方面的显着差异，确定并讨论了六个拼车原型。本文讨论了与可负担性、公平性和交通竞争相关的已识别集群的影响。