UZH-Logo

Maintenance Infos

Movement similarity assessment using symbolic representation of trajectories


Dodge, S; Laube, P; Weibel, Robert (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science, 26(9):1563-1588.

Abstract

This paper describes a novel approach for finding similar trajectories, using trajectory segmentation based on movement parameters such as speed, acceleration, or direction. First, a segmentation technique is applied to decompose trajectories into a set of segments with homogeneous characteristics with respect to a particular movement parameter. Each segment is assigned to a movement parameter class, representing the behavior of the movement parameter. Accordingly, the segmentation procedure transforms a trajectory to a sequence of class labels, that is, a symbolic representation. A modified version of edit distance, called Normalized Weighted Edit Distance (NWED) is introduced as a similarity measure between different sequences. As an application, we demonstrate how the method can be employed to cluster trajectories. The performance of the approach is assessed in two case studies using real movement datasets from two different application domains, namely, North Atlantic Hurricane trajectories and GPS tracks of couriers in London. Three different experiments have been conducted that respond to different facets of the proposed techniques, and that compare our NWED measure to a related method.

This paper describes a novel approach for finding similar trajectories, using trajectory segmentation based on movement parameters such as speed, acceleration, or direction. First, a segmentation technique is applied to decompose trajectories into a set of segments with homogeneous characteristics with respect to a particular movement parameter. Each segment is assigned to a movement parameter class, representing the behavior of the movement parameter. Accordingly, the segmentation procedure transforms a trajectory to a sequence of class labels, that is, a symbolic representation. A modified version of edit distance, called Normalized Weighted Edit Distance (NWED) is introduced as a similarity measure between different sequences. As an application, we demonstrate how the method can be employed to cluster trajectories. The performance of the approach is assessed in two case studies using real movement datasets from two different application domains, namely, North Atlantic Hurricane trajectories and GPS tracks of couriers in London. Three different experiments have been conducted that respond to different facets of the proposed techniques, and that compare our NWED measure to a related method.

Citations

15 citations in Web of Science®
23 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

110 downloads since deposited on 02 Apr 2012
33 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2012
Deposited On:02 Apr 2012 09:00
Last Modified:05 Apr 2016 15:33
Publisher:Taylor & Francis
ISSN:1365-8816 (P) 1365-8824 (E)
Additional Information:This is an electronic version of an article published in Dodge, S; Laube, P; Weibel, R (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science:online. Journal of Geographical Information Science: http://www.tandfonline.com/loi/tgis20.
Publisher DOI:https://doi.org/10.1080/13658816.2011.630003
Permanent URL: https://doi.org/10.5167/uzh-58038

Download

[img]
Preview
Content: Accepted Version
Language: English
Filetype: PDF
Size: 3MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations