A Balanced Assignment Mechanism for Online Taxi Recommendation

Majority of taxi recommender systems mainly focused on satisfaction of passengers without considering fairness in assignment of taxi drivers. In this paper we propose a balanced assignment mechanism for online taxi recommendation (BAMOTR). BAMOTR provides a mechanism for fair assignment of drivers at some locations with specific routes to pick up passengers and ensures a short waiting time for passengers. Fair assignment is intended to minimize the differences in income among the taxi drivers. Analysis shows out that fair assignment of drivers and shortening the time the passenger wait before pick up is a trade-off problem. In this paper, we set a regulatory factor that can adjust the trade-off between fair assignment of drivers and shortening of waiting time of passengers. We also propose an efficient range refinement algorithm to solve online taxi recommendation problem in BAMOTR. It is theoretically and experimentally proved that range refinement algorithm ensures the same recommendation result as brute-force algorithm, however it greatly reduces the time overhead. We validate the performances of BAMOTR with extensive evaluations. Experimental results show that BAMOTR achieve better recommendation fairness than compared approaches and guarantee a short waiting time for passengers to be picked up.