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 language||English (US)|
|Number of pages||26|
|Journal||International Journal of Geographical Information Science|
|State||Published - Sep 2012|
Bibliographical noteFunding Information:
This research was partly funded by the Research Fund (‘Forschungskredit’) of the University of Zurich. We express our gratitude to Dr. Goce Trajcevski from the Department of Electrical Engineering and Computer Science, Northwestern University, for his valuable input at the initial stage of this research, and Jay Bregman (eCourier company, UK) for providing us with the courier data.
- Movement similarity
- movement parameter
- movement patterns
- trajectory clustering
- trajectory segmentation