An Incentive-Compatible and Efficient Mechanism for Matching and Pricing in Ride-Sharing

Abstract

As a novel and economic transportation way, ride-sharing has attracted more and more passengers and drivers to participate. How to match passengers with drivers efficiently has become a key issue. Specifically, drivers are usually heterogeneous with different costs, and they may behave strategically (e.g. reveal their private cost information untruthfully) in order to make more profits. Drivers’ strategic behavior may lead to inefficient matching, and thus result in the loss of social welfare of ride-sharing platform and drivers. In this paper, we intend to solve this issue by designing an incentive-compatible and efficient mechanism, which can match passengers with drivers and determine the payments to drivers in order to maximize the social welfare while ensuring drivers reveal their cost information truthfully. Specifically, we design an order matching algorithm with a branch and bound based route planning algorithm to accelerate the matching process. Meanwhile, we compute the payments to drivers based on the second pricing mechanism. In so doing, we propose a second pricing based ride-sharing mechanism (SPRM), which satisfies incentive compatibility, individual rationality, budget balance and computational efficiency. We further run extensive experiments to evaluate our mechanism based on the real Manhattan taxi order data and vehicle fuel consumption data. The experimental results show that SPRM can guarantee drivers’ profits and improve the ratio of drivers’ participation and the ratio of served orders, and eventually achieve greater social welfare than two typical benchmark approaches, GPri and ND.