Movement similarity assessment using symbolic representation of trajectories

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 di erent 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 di erent application domains, namely, North Atlantic Hurricane trajectories and GPS tracks of couriers in London. Three di erent experiments have been conducted that respond to di erent facets of the proposed techniques, and that compare our NWED measure to a related method.

Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

“…However, edit distance, which is the similarity measurement method of multi sub-time interval correspondence, does not require correspondence between points and the points of the two trajectories, which can reflect the structural differences between the trajectory sequences and determine the similarities of whole trajectories. At present, there are few studies on the application of edit distance to GPS trajectories [27,28]. The specific situation of edit distance will be discussed in Section 3.1.…”

“…Because the sampling time of the GPS data is inconsistent, the distance between the sampling points is different, and the operation cost must be modified. Dodge et al [27] represented trajectories by a string representation according to movement parameters, such as speed, acceleration, or direction. However, the method focused on the parameters describing the dynamic characteristics of movement and did not address trajectory similarity for geospatial space.…”

Computational Streetscapes

“…In recent years, these schemes have been supplemented by motion capture approaches that blend very high-resolution tracked performance data with kinematic solvers [162,163]. These developments were incredibly useful in animation, as they facilitated the calibration of low-level movement directly to real data [164][165][166], and they allowed for significant detail of ambulation, gait, and body language to be encoded in characters [167][168][169], while also solving for the collisions and steering that are usually required while traversing busy street scenes.…”

Semantic management of moving objects: A vision towards smart mobility

“…For example, in some situations the task of automatic place labeling can be considered as a multi-class classification task (Do & Gatica-Perez, 2014). Moreover, the aggregation and clustering of trajectories (e.g., see Dodge et al, 2012) are of importance to understand the meaning behind the trajectories and try to correlate them with people's trends, habits and behavior patterns, or to understand the behavior of crowds (Wirz et al, 2013). According to Pejovic and Musolesi (2013), ''As devices become increasingly intelligent, their capabilities evolve beyond inferring context to anticipating it.''…”

“…The associated labels can then be exploited by a pattern language that can be used to match and rewrite symbolic trajectories. In Dodge, Laube, and Weibel (2012), the authors propose a symbolic representation of trajectories based on the identification of similar movement parameters (e.g., speed, acceleration, direction).…”