Coalitional Game Based Carpooling Algorithms for Quality of Experience

Abstract

Carpooling service is an effective solution to balance the limited number of taxicabs and the soaring demands from users, Thus, how to motivate more passengers to participate in carpooling is essential, especially in extreme weather or in rush hours. Most of existing works focus on improving the availability, convenience and security of carpooling service, while ignoring to guarantee the quality of experience of passengers. In this work, we focus on how to fulfill the expected sojourn time of passengers in carpooling service using coalition game. We formulate the QoE guarantee problem as a benefit allocation problem. To solve the problem, we quantify the impatience of passengers due to detouring time delay, depending on their own expected sojourn time and expected compensation per unit time of delay. The algorithm named PCA is proposed to minimize the impatience of all passengers, under which we calculate the compensation for passengers based on Shapley value. We prove that PCA can guarantee the fairness of passengers. Extensive simulation results demonstrate that PCA can minimize the impatience of passengers. Moreover, compared with the existing algorithm DST, PCA can reduce the payment of each passenger by 14.4 percent averagely with only 13.3 percent loss of driver's revenue. However, the least expected revenue of the driver can still be fulfilled, which produces a win-win solution for both passengers and drivers in carpooling.