Sample-based Prophet for Online Ride-sharing with Fairness

Baoju Li, En Wang, Funing Yang, Yongjian Yang, Wenbin Liu, Zijie Tian, Junyu Liu, Wanbo Zheng

Abstract: The prosperity of industrialization urges modern ride-sharing platforms to gain profit from efficient management of their resources. Although ride-sharing allows sharing costs and promotes the traffic efficiency by making better use of vehicle capacities, dealing with large amounts of online taxi orders is an inevitable challenge in the current transportation systems, where all drivers have to make immediate and irrevocable decisions about whether to accept current order in a parallel way. Furthermore, in order to achieve global fairness, it is critical for an algorithm to function whenever the first order gets on-line without any observation stage. In this paper, we formulate this online user selection problem as a prophet inequality for independent identically distributed random variables from an unknown distribution. We construct a sample set to avoid the observation stage in an online decision process. Considering the driver-centered ride-sharing scenario, a route schedule algorithm and a sample-driven algorithm with a guarantee of lower bound are proposed to concurrently guide taxi drivers to accept taxi orders and achieve global fairness at the meantime. Finally, we conduct extensive evaluations based on three real-world data sets. The results verify the effectiveness of our proposed algorithm on improving the overall profit, increasing accepted orders and reducing the unoccupied time of the vehicle under the valid ride-sharing constraints.