Similarity of trajectories taking into account geographic context

The movements of animals, people, and vehicles are embedded in a geographic context. This context influences the movement and may cause the formation of certain behavioral responses. Thus, it is essential to include context parameters in the study of movement and the development of movement pattern analytics. Advances in sensor technologies and positioning devices provide valuable data not only of moving agents but also of the circumstances embedding the movement in space and time. Developing knowledge discovery methods to investigate the relation between movement and its surrounding context is a major challenge in movement analysis today. In this paper we show how to integrate geographic context into the similarity analysis of movement data. For this, we discuss models for geographic context of movement data. Based on this we develop simple but efficient context-aware similarity measures for movement trajectories, which combine a spatial and a contextual distance. These are based on well-known similarity measures for trajectories, such as the Hausdorff, Fréchet, or equal time distance. We validate our approach by applying these measures to movement data of hurricanes and albatross

Computing the Similarity Between Moving Curves

In this paper we study similarity measures for moving curves which can, for example, model changing coastlines or retreating glacier termini. Points on a moving curve have two parameters, namely the position along the curve as well as time. We therefore focus on similarity measures for surfaces, specifically the Fr\'echet distance between surfaces. While the Fr\'echet distance between surfaces is not even known to be computable, we show for variants arising in the context of moving curves that they are polynomial-time solvable or NP-complete depending on the restrictions imposed on how the moving curves are matched. We achieve the polynomial-time solutions by a novel approach for computing a surface in the so-called free-space diagram based on max-flow min-cut duality

A Measure of Similarity Between Trajectories of Vessels

The measurement of similarity between trajectories of vessels is one of the kernel problems that must be addressed to promote the development of maritime intelligent traffic system (ITS). In this study, a new model of trajectory similarity measurement was established to improve the data processing efficiency in dynamic application and to reflect actual sailing behaviors of vessels. In this model, a feature point detection algorithm was proposed to extract feature points, reduce data storage space and save computational resources. A new synthesized distance algorithm was also created to measure the similarity between trajectories by using the extracted feature points. An experiment was conducted to measure the similarity between the real trajectories of vessels. The growth of these trajectories required measurements to be conducted under different voyages. The results show that the similarity measurement between the vessel trajectories is efficient and correct. Comparison of the synthesized distance with the sailing behaviors of vessels proves that results are consistent with actual situations. The experiment results demonstrate the promising application of the proposed model in studying vessel traffic and in supplying reliable data for the development of maritime ITS

Context-aware similarity of trajectories

The movement of animals, people, and vehicles is embedded in a geographic context. This context influences the movement. Most analysis algorithms for trajectories have so far ignored context, which severely limits their applicability. In this paper we present a model for geographic context that allows us to integrate context into the analysis of movement data. Based on this model we develop simple but efficient context-aware similarity measures. We validate our approach by applying these measures to hurricane trajectories. Keywords: Movement data – geographic context – similarity measure

Context-aware similarity of trajectories

The movement of animals, people, and vehicles is embedded in a geographic context. This context influences the movement. Most analysis algorithms for trajectories have so far ignored context, which severely limits their applicability. In this paper we present a model for geographic context that allows us to integrate context into the analysis of movement data. Based on this model we develop simple but efficient context-aware similarity measures. We validate our approach by applying these measures to hurricane trajectories. Keywords: Movement data – geographic context – similarity measure