  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. The deployment of autonomous cars in the near future will surely
change the way people are traveling. It is even more promising for a
ridesharing service, since it will be easier and cheaper for a company to
handle a fleet of autonomous cars that can serve the demands of different
passengers.
  We argue that user satisfaction should be the main objective when trying to
find the best assignment of passengers to vehicles and the determination of
their routes. Moreover, the model of user satisfaction should be rich enough to
capture the traveling distance, cost, and other factors as well. We show that
it is more important to capture a rich model of human satisfaction than peruse
an optimal performance. That is, we developed a practical algorithm for
assigning passengers to vehicles, which outperforms assignment algorithms that
are optimal, but use a simpler satisfaction model.
  To the best of our knowledge, this is the first paper to exclusively
concentrate on a rich and realistic function of user satisfaction as the
objective, which is (arguably) the most important aspect to consider for
achieving a widespread adaption of ridesharing services.
