Next Article in Journal
Open Polar Server (OPS)—An Open Source Infrastructure for the Cryosphere Community
Previous Article in Journal
Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method
Article Menu

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2016, 5(3), 29; doi:10.3390/ijgi5030029

Extracting Stops from Noisy Trajectories: A Sequence Oriented Clustering Approach

1
State Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, China
2
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 26 January 2016 / Revised: 26 February 2016 / Accepted: 29 February 2016 / Published: 9 March 2016
View Full-Text   |   Download PDF [2707 KB, uploaded 9 March 2016]   |  

Abstract

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top