Balancing Efficiency and Fairness in On-Demand Ridesourcing

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Nixie S. Lesmana, Xuan Zhang, Xiaohui Bei


We investigate the problem of assigning trip requests to available vehicles in on-demand ridesourcing. Much of the literature has focused on maximizing the total value of served requests, achieving efficiency on the passengers’ side. However, such solutions may result in some drivers being assigned to insufficient or undesired trips, therefore losing fairness from the drivers’ perspective.

In this paper, we focus on both the system efficiency and the fairness among drivers and quantitatively analyze the trade-offs between these two objectives. In particular, we give an explicit answer to the question of whether there always exists an assignment that achieves any target efficiency and fairness. We also propose a simple reassignment algorithm that can achieve any selected trade-off. Finally, we demonstrate the effectiveness of the algorithms through extensive experiments on real-world datasets.