Visually exploring movement data via similarity-based analysis

Abstract

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuri ng efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for "hands-on" analysis for trajectory data

A Novel Trajectory Similarity Evaluation Method in VANETs

“…The speed information has been considered in similarity measuring in [7]. This paper integrates these factories into the spatiotemporal similarity evaluation method.…”

“…Pelekis et al [7] argue that driving parameters (speed, acceleration and direction) also affect the similarity between trajectories. Parent el al.…”

“…In the recent years, researchers realized that temporal factors are also important and should be considered in similar trajectories evaluation [2]. Driving parameters (speed, acceleration and direction) of trajectories also affect the similarity between trajectories [7]. The significance of each point on a trajectory may be different for a special mobile user who is requesting similar trajectories.…”

Movement Pattern Analysis Based on Sequence Signatures

“…A large body of research has been conducted to express the significance of visualization in understanding movements in different domains (e.g., [36][37][38][39][40][41]). Detecting, understanding, and visualizing movement patterns are not limited to certain applications.…”

“…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.…”