Semantic trajectory mining for location prediction

Authors:
Josh Jia-Ching Ying;Wang-Chien Lee;Tz-Chiao Weng;Vincent S. Tseng
Affiliations:
National Cheng Kung University, Tainan City, Taiwan (R.O.C.);Pennsylvania State University, University Park, PA;National Cheng Kung University, Tainan City, Taiwan (R.O.C.);National Cheng Kung University, Tainan City, Taiwan (R.O.C.)
Venue:
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Year:
2011

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Abstract

Research on predicting movements of mobile users has attracted a lot of attentions in recent years. Many of those prediction techniques are developed based only on geographic features of mobile users' trajectories. In this paper, we propose a novel approach for predicting the next location of a user's movement based on both the geographic and semantic features of users' trajectories. The core idea of our prediction model is based on a novel cluster-based prediction strategy which evaluates the next location of a mobile user based on the frequent behaviors of similar users in the same cluster determined by analyzing users' common behavior in semantic trajectories. Through a comprehensive evaluation by experiments, our proposal is shown to deliver excellent performance.