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.
Shuo Shang Zhewei Wei Ji-Rong Wen Shunzhi Zhu
Department of Computer Science,China University of Petroleum-Beijing,Beijing,China Beijing Key Laboratory of Big-data Management and Analysis,School of Information,Renmin University o Xiamen University of Technology,Xiamen,China
国际会议
International Asia-Pacific Web Conference(第18届国际亚太互联网大会)
苏州
英文
3-14
2016-09-23（万方平台首次上网日期，不代表论文的发表时间）