One Way Distance: For Shape Based Similarity Search of Moving Object Trajectories

Abstract

An interesting issue in moving object databases is to find similar trajectories of moving objects. Previous work on this topic focuses on movement patterns (trajectories with time dimension) of moving objects, rather than spatial shapes (trajectories without time dimension) of their trajectories. In this paper we propose a simple and effective way to compare spatial shapes of moving object trajectories. We introduce a new distance function based on "one way distance" (OWD). Algorithms for evaluating OWD in both continuous (piece wise linear) and discrete (grid representation) cases are developed. An index structure for OWD in grid representation, which guarantees no false dismissals, is also given to improve the efficiency of similarity search. Empirical studies show that OWD out-performs existent methods not only in precision, but also in efficiency. And the results of OWD in continuous case can be approximated by discrete case efficiently.

Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

“…Most of the techniques proposed to date are looking for similarities of the geometric shape of the trajectories based on a distance function. Examples include the Edit Distance on Real sequence (EDR) (Chen, Özsu, & Oria, 2005), One-Way Distance (OWD) (Lin & Su, 2008), Euclidean and Time Wrapping distance and Longest Common Subsequence (LCSS) (Vlachos, Gunopulos, & Kollios, 2002). However, we are more interested in finding similarities in movement behavior of different types of moving objects.…”

Movement Pattern Analysis Based on Sequence Signatures

“…Among them, some contributed to the clustering of moving objects (e.g., [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]), the mining of movement patterns (e.g., [34,38,[43][44][45]) and the exploration of similarity of moving objects (e.g., [41,[46][47][48][49]). The idea behind the approach proposed in this paper is to contribute to all three classes of knowledge discovery, i.e., mining patterns, similarity assessment, and clustering.…”

Online Clustering Algorithms for Semantic-Rich Network Trajectories

“…For trajectories without constraints, trajectories are typically represented by either a set or sequence of locations (sampling points), for which similarity metrics have been proposed in [8][9][10][11][12][13] and [14][15][16][17], respectively.…”

“…While distance measures satisfying the above requirements for trajectories without road network constraints have been actively studied [12][13][14][15], the distance measure for network-constrained trajectories has only recently been proposed in our preliminary work.…”