  Recommending routes by their probability of having a rider has long been the
goal of conventional route recommendation systems. While this maximizes the
platform-specific criteria of efficiency, it results in sub-optimal outcomes
with the disparity among the income of drivers who work for similar time
frames. Pioneer studies on fairness in ridesharing platforms have focused on
algorithms that match drivers and riders. However, these studies do not
consider the time schedules of different riders sharing a ride in the
ridesharing mode. To overcome this shortcoming, we present the first route
recommendation system for ridesharing networks that explicitly considers
fairness as an evaluation criterion. In particular, we design a routing
mechanism that reduces the inequality among drivers and provides them with
routes that have a similar probability of finding riders over a period of time.
However, while optimizing fairness the efficiency of the platform should not be
affected as both of these goals are important for the long-term sustainability
of the system. In order to jointly optimize fairness and efficiency we consider
repositioning drivers with low income to the areas that have a higher
probability of finding riders in future. While applying driver repositioning,
we design a future-aware policy and allocate the areas to the drivers
considering the destination of requests in the corresponding area. Extensive
simulations on real-world datasets of Washington DC and New York demonstrate
superior performance by our proposed system in comparison to the existing
baselines.
