Top-k Taxi Recommendation in Realtime Social-Aware Ridesharing Services - Fu, Xiaoyi; Huang, Jinbin; Lu, Hua; Xu, Jianliang; Li, Yafei (Advances in Spatial and Temporal Databases, 2017)
Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In this paper, we introduce social-awareness into realtime ridesharing services. In particular, upon receiving a user’s trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers’ cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services.