Dynamic Ridesharing With Minimal Regret: Towards an Enhanced Engagement Among Three Stakeholders

Abstract

In dynamic ridesharing, the platform serves as the mediator by tailoring the assignment result between workers and riders with a focus on a certain objective. Existing studies generally focus on either one or two stakeholders when modelling the problem while the wellbeing of the other parties may be ignored or even undermined. For example, purely maximizing the total revenue of the ridesharing platform may cause the loss of riders and in turn lead to a low served rate, because those expensive orders will be processed in priority. In this paper, we for the first time study how to incorporate the willingness of all stakeholders (i.e., the platform, workers and riders). Given a set of workers and a set of rider requests, we aim to return the matchable worker-rider pairs in order to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regret</i> . Specifically, two types of regret are defined: (i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">served rate regret</i> , which refers to the rate of unserved requests, catering for the reputation and profit of the platform and workers; (ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">revenue regret</i> , which considers the portion of revenue loss from unserved riders, catering for the focus of workers and riders in the trip schedule. We prove the NP-hardness of this problem. To tackle this problem, we first propose a dynamic programming insertion algorithm to improve the efficiency of inserting a rider request into a trip schedule of a worker. Furthermore, two kinds of heuristic algorithms are devised to match rider requests with workers effectively. Comprehensive experiments on two real-world datasets verify the effectiveness, efficiency and scalability of our solutions in dealing with different supply-demand relationships in practice.