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Open AccessArticle

Extracting Stops from Noisy Trajectories: A Sequence Oriented Clustering Approach

State Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, China
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(3), 29;
Received: 26 January 2016 / Revised: 26 February 2016 / Accepted: 29 February 2016 / Published: 9 March 2016
PDF [2707 KB, uploaded 9 March 2016]


Trajectories, representing the movements of objects in the real world, carry significant stop/move semantics. The detection of trajectory stops poses a critical problem in the study of moving objects and becomes even more challenging due to the inevitable noise recorded along with true data. To extract stops with a variety of shapes and sizes from single trajectories with noise, this paper presents a sequence oriented clustering approach, in which noise points within the sequence of a stop can be identified and classified as a part of the stop. In our method, two key concepts are first introduced: (1) a core sequence that defines sequence density based not only on proximity in space but also continuity in time as well as the duration over time; and (2) an Eps-reachability sequence that aggregates core sequences that overlap or meet over time. Then, three criteria are presented to merge Eps-reachability sequences interrupted by noise. Further, an algorithm, called SOC (Sequence Oriented Clustering), is developed to automatically extract stops from a single trajectory. In addition, a reachability graph is designed that visually illustrates the spatio-temporal clustering structure and levels of a trajectory. Finally, the proposed algorithm is evaluated against two baseline methods through extensive experiments based on real world trajectories, some with serious noise, and the results show that our approach is fairly effective in recognizing trajectory stops. View Full-Text
Keywords: trajectory stop; core sequence; reachability graph; sequence oriented clustering trajectory stop; core sequence; reachability graph; sequence oriented clustering

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Xiang, L.; Gao, M.; Wu, T. Extracting Stops from Noisy Trajectories: A Sequence Oriented Clustering Approach. ISPRS Int. J. Geo-Inf. 2016, 5, 29.

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