  The emergence of ride-sourcing platforms has brought an innovative
alternative in transportation, radically changed travel behaviors, and
suggested new directions for transportation planners and operators. This paper
provides an exploratory analysis on the operations of a ride-sourcing service
using large-scale data on service performance. Observations over multiple days
in Singapore suggest reproducible demand patterns and provide empirical
estimates of fleet operations over time and space. During peak periods, we
observe significant increases in the service rate along with surge price
multipliers. We perform an in-depth analysis of fleet utilization rates and are
able to explain daily patterns based on drivers' behavior by involving the
number of shifts, shift duration, and shift start and end time choices. We also
evaluate metrics of user experience, namely waiting and travel time
distribution, and explain our empirical findings with distance metrics from
driver trajectory analysis and congestion patterns. Our results of empirical
observations on actual service in Singapore can help to understand the
spatiotemporal characteristics of ride-sourcing services and provide important
insights for transportation planning and operations.
