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An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories

Key Laboratory of Spatial Information Processing and Application System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing100190, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(3), 63;
Received: 18 December 2016 / Revised: 17 February 2017 / Accepted: 21 February 2017 / Published: 25 February 2017
PDF [4828 KB, uploaded 2 March 2017]


With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based spatial clustering of applications with noise) is a classical density-based algorithm used to find the high-density areas in space, and different derivative methods of this algorithm have been proposed to find the stops in trajectories. However, most of these methods required a manually-set threshold, such as the speed threshold, for each feature variable. In our research, we first defined our new concept of move ability. Second, by introducing the theory of data fields and by taking our new concept of move ability into consideration, we constructed a new, comprehensive, hybrid feature–based, density measurement method which considers temporal and spatial properties. Finally, an improved DBSCAN algorithm was proposed using our new density measurement method. In the Experimental Section, the effectiveness and efficiency of our method is validated against real datasets. When comparing our algorithm with the classical density-based clustering algorithms, our experimental results show the efficiency of the proposed method. View Full-Text
Keywords: trajectory data; stops and moves; improved DBSCAN algorithm; temporal and spatial properties trajectory data; stops and moves; improved DBSCAN algorithm; temporal and spatial properties

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Luo, T.; Zheng, X.; Xu, G.; Fu, K.; Ren, W. An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories. ISPRS Int. J. Geo-Inf. 2017, 6, 63.

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