Ensuring Service Fairness in Taxi Fleet Management

On-demand mobility services form a major section of the transport system, especially in metropolitan cities. Contemporary on-demand systems work in a partially coordinated manner where the drivers follow their own passenger finding strategies. This leads to sub-optimal performance at the company level due to a spatial-temporal supply-demand mismatch. A coordinated fleet of taxis that also employs the knowledge of predicted future demand is being seen as a promising upgrade to contemporary systems. However, for this system to work, the drivers must see the clear benefits of switching from the contemporary system to the coordinated system. In other terms, the drivers must feel that the system is fair. In this paper, we have built a framework to simulate an on-demand taxi system working in a coordinated manner. Our objective is to ensure company profit maximization while improving service fairness in terms of driver profit disparity reduction as well as increasing the scheduling guarantee for drivers who are following the recommendation of the system. We show that the proposed model can serve 49.8% more demand with 71% higher mean driver profit, 83% lower profit disparity and a 39.8% higher scheduling guarantee.