APDS: A framework for discovering movement pattern from trajectory database

Guan Yuan, Zhongqiu Wang, Zhixiao Wang, Fukai Zhang, Li Yuan and Jian Zhang

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 11, 1550147719888164

Abstract: Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

Keywords: Moving objects; activity pattern discovery; trajectory clustering; spatial–temporal data mining (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:11:p:1550147719888164

DOI: 10.1177/1550147719888164

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