A Carpooling Recommendation System for Taxicab Services

Carpooling taxicab services hold the promise of providing additional transportation supply, especially in the extreme weather or rush hour when regular taxicab services are insufficient. Although many recommendation systems about regular taxicab services have been proposed recently, little research, if any, has been done to assist passengers to find a successful taxicab ride with carpooling. In this paper, we present the first systematic work to design a unified recommendation system for both the regular and carpooling services, called CallCab, based on a data-driven approach. In response to a passenger's real-time request, CallCab aims to recommend either: 1) a vacant taxicab for a regular service with no detour or 2) an occupied taxicab heading to the similar direction for a carpooling service with the minimum detour, yet without assuming any knowledge of destinations of passengers already in taxicabs. To analyze these unknown destinations of occupied taxicabs, CallCab generates and refines taxicab trip distributions based on GPS data sets and context information collected in the existing taxicab infrastructure. To improve CallCab's efficiency to process such a big data set, we augment the efficient MapReduce model with a Measure phase tailored for our CallCab. Finally, we design a reciprocal price mechanism to facilitate the taxicab carpooling implementation in the real world. We evaluate CallCab with a real-world data set of 14000 taxicabs, and results show that compared with the ground truth, CallCab reduces 60% of the total mileage to deliver all passengers and 41% of passenger's waiting time. Our price mechanism reduces 23% of passengers' fares and increases 28% of drivers' profits simultaneously.