Probabilistic Nearest Neighbor Query in Traffic-Aware Spatial Networks

Travel planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of finding probabilistic nearest neighbors and planning the corresponding travel routes in traffic-aware spatial networks (TANN queries) to avoid traffic congestions. We propose and study two probabilistic TANN queries: (1) a time-threshold query like "what is my closest restaurant with the minimum congestion probability to take at most 45min?", and (2) a probability-threshold query like "what is the fastest path to my closest petrol station whose congestion probability is less than 20%?". We believe that this type of queries may benefit users in many popular mobile applications, such as discovering nearby points of interest and planning convenient travel routes for users. The TANN queries are challenged by two difficulties: (1) how to define probabilistic metrics for nearest neighbor queries in traffic-aware spatial networks, and (2) how to process the TANN queries efficiently under different query settings. To overcome these challenges, we define a series of new probabilistic metrics and develop two efficient algorithms to compute the TANN queries. The performances of TANN queries are verified by extensive experiments on real and synthetic spatial data.