Movement similarity assessment using symbolic representation of trajectories

Somayeh Dodge, Patrick Laube, Robert Weibel

Research output: Contribution to journalArticle

77 Scopus citations


This article describes a novel approach for finding similar trajectories, using trajectory segmentation based on movement parameters (MPs) 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 MP. Each segment is assigned to a movement parameter class (MPC), representing the behavior of the MP. 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.

Original languageEnglish (US)
Pages (from-to)1563-1588
Number of pages26
JournalInternational Journal of Geographical Information Science
Issue number9
StatePublished - Sep 1 2012


  • Movement similarity
  • movement parameter
  • movement patterns
  • trajectory clustering
  • trajectory segmentation

Fingerprint Dive into the research topics of 'Movement similarity assessment using symbolic representation of trajectories'. Together they form a unique fingerprint.

  • Cite this