Discovering personalized routes from trajectories
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Constructing popular routes from uncertain trajectories
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting large-scale check-in data to recommend time-sensitive routes
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Exploring pattern-aware travel routes for trajectory search
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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In this paper, we develop a framework of trajectory search called pattern-aware trajectory search abbreviated as PATS). Given a set of trajectories, potential regions are extracted first and potential regions are viewed as popular regions interested by users. Furthermore, potential regions are organized as a region transition graph where each vertex is a potential region and edges capture sequential travels of potential regions from a set of trajectories given. By exploring the concept of random walk, the attractiveness of a potential region is derived. In light of attractiveness of potential regions the attractiveness of a trajectory is formulated and PATS will return top-K trajectories according to their attractiveness. We evaluated our framework by a real GPS dataset. Experimental results show that PATS is able to retrieve trajectories interested by users.