Real-Time Personalized Taxi-Sharing

Xiaoyi Duan, Cheqing Jin, Xiaoling Wang, Aoying Zhou, Kun Yue

Abstract: Taxi-sharing is an efficient way to improve the utility of taxis by allowing multiple passengers to share a taxi. It also helps to relieve the traffic jams and air pollution. It is common that different users may have different attitudes towards the taxi-sharing scheduling plan, such as the fee to be paid and the additional time to the destination. However, this property has not been paid enough attention to in the traditional taxi-sharing systems – the traditional focus is how to decrease the travel distance. We study the problem of personalized taxi-sharing in this paper, with the consideration of each passenger’s preference in payment, travel time and waiting time. We first define the satisfaction degree of each party involved in the scheduling plan, based on which two goals are defined to evaluate the overall plan, including MaxMin and MaxSum. Subsequently, we devise a two-phase framework to deal with this problem. The statistical information gathered during the offline phase will be used to hasten query processing during the online phase. Experimental reports upon the real dataset illustrate the effectiveness and efficiency of the proposed method.