  The ridesharing economy is experiencing rapid growth and innovation.
Companies such as Uber and Lyft are continuing to grow at a considerable pace
while providing their platform as an organizing medium for ridesharing
services, increasing consumer utility as well as employing thousands in
part-time positions. However, many challenges remain in the modeling of
ridesharing services, many of which are not currently under wide consideration.
In this paper, an agent-based model is developed to simulate a ridesharing
service in the Washington D.C. metropolitan region. The model is used to
examine levels of utility gained for both riders (customers) and drivers
(service providers) of a generic ridesharing service. A description of the
Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) is provided,
as well as a description of a typical simulation run. We investigate the
financial gains of drivers for a 24-hour period under two scenarios and two
spatial movement behaviors. The two spatial behaviors were random movement and
Voronoi movement, which we describe. Both movement behaviors were tested under
a stationary run conditions scenario and a variable run conditions scenario. We
find that Voronoi movement increased drivers' utility gained but that emergence
of this system property was only viable under variable scenario conditions.
This result provides two important insights: The first is that driver movement
decisions prior to passenger pickup can impact financial gain for the service
and drivers, and consequently, rate of successful pickup for riders. The second
is that this phenomenon is only evident under experimentation conditions where
variability in passenger and driver arrival rates are administered.
