  Transportation services play a crucial part in the development of modern
smart cities. In particular, on-demand ridesharing services, which group
together passengers with similar itineraries, are already operating in several
metropolitan areas. These services can be of significant social and
environmental benefit, by reducing travel costs, road congestion and CO2
emissions.
  Unfortunately, despite their advantages, not many people opt to use these
ridesharing services. We believe that increasing the user satisfaction from the
service will cause more people to utilize it, which, in turn, will improve the
quality of the service, such as the waiting time, cost, travel time, and
service availability. One possible way for increasing user satisfaction is by
providing appropriate explanations comparing the alternative modes of
transportation, such as a private taxi ride and public transportation. For
example, a passenger may be more satisfied from a shared-ride if she is told
that a private taxi ride would have cost her 50% more. Therefore, the problem
is to develop an agent that provides explanations that will increase the user
satisfaction.
  We model our environment as a signaling game and show that a rational agent,
which follows the perfect Bayesian equilibrium, must reveal all of the
information regarding the possible alternatives to the passenger. In addition,
we develop a machine learning based agent that, when given a shared-ride along
with its possible alternatives, selects the explanations that are most likely
to increase user satisfaction. Using feedback from humans we show that our
machine learning based agent outperforms the rational agent and an agent that
randomly chooses explanations, in terms of user satisfaction.
