APDS: A framework for discovering movement pattern from trajectory database. (November 2019)

• Record Type: Journal Article
• Authors: Yuan, Guan; Wang, Zhongqiu; Wang, Zhixiao; Zhang, Fukai; Yuan, Li; Zhang, Jian
• Is Part Of: International journal of distributed sensor networks. Volume 15:Number 11(2019)
• Publication Date: 2019-11
• Subjects: Moving objects -- activity pattern discovery -- trajectory clustering -- spatial–temporal data mining; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147719888164
• Languages: English
• ISSNs: 1550-1329

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.