  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.
