ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (1): 111-122.doi: 10.7544/issn1000-1239.2017.20150784

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Discovering Common Behavior Using Staying Duration on Semantic Trajectory

Guo Limin1, Gao Xu2,3, Wu Bin2, Guo Haoming2, Xu Huaiye2, Wei Yanyan2, Wang Zhixin2, Yan Li2, Tian Mu2   

  1. 1(Beijing University of Technology, Beijing 100124); 2(Institute of Software, Chinese Academy of Sciences, Beijing 100190); 3(University of Chinese Academy of Sciences, Beijing 100049)
  • Online:2017-01-01

Abstract: With the advancement of mobile computing technology and the widespread use of GPS-enabled mobile devices, research on semantic trajectories has attracted a lot of attentions in recent years, and the semantic trajectory pattern mining is one of the most important issues. Most existing methods discover the similar behavior of moving objects through the analysis of sequences of stops. However, these methods have not considered the duration of staying on a stop which affects the accuracy to distinguish different behavior patterns. In order to solve the problem, this paper proposes a novel approach for discovering common behavior using staying duration on semantic trajectory (DSTra) which can easily differentiate trajectory patterns. DSTra can be used to detect the group that has similar lifestyle, habit or behavior patterns. Semantic trajectory patterns of each moving object are mined firstly. Then, the time-weight based pattern similarity measurement is designed. After that, a hierarchical clustering method with pruning strategy is proposed, where each cluster represents the common behavior patterns from moving objects. Finally, experiments on both real-world dataset and synthetic dataset demonstrate the effectiveness, precision and efficiency of DSTra.

Key words: semantic trajectory, staying duration, semantic trajectory pattern, pattern similarity, moving object clustering

CLC Number: