Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. Issue 3 (4th May 2017)

• Record Type: Journal Article
• Authors: Sharif, Mohammad; Alesheikh, Ali Asghar
• Is Part Of: GIScience & remote sensing. Volume 54:Issue 3(2017)
• Page Start: 426
• Page End: 452
• Publication Date: 2017-05-04
• Subjects: context -- similarity measurement -- pattern discovery -- trajectory -- dynamic time warping (DTW); Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2017.1278644
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Analyzing the spatial behaviors of moving-point objects (MPOs) and discovering their movement patterns have been of great interest to the geographic information science community recently. These interests can be explored through analyzing similarities in the MPO trajectories. Because movements of objects take place in various contexts, their trajectories are also highly influenced by such contexts. Therefore, it is essential to fully understand the contexts and to realize how they can be incorporated into movement analysis. This article first proposes a taxonomy for contexts. Then, a modified version of dynamic time warping called context-based dynamic time warping (CDTW) is introduced, to contextually assess the multidimensional weighted similarities of trajectories. Ultimately, the results of similarity searches are utilized in discovering the relative movement patterns of the MPOs. To evaluate the performance and effectiveness of our proposed CDTW method, we run several experiments on real datasets that were obtained from commercial airplanes in a constrained Euclidean space, taking into account contextual information. Specifically, these experiments were conducted to explore the role of contexts and their interactions in similarity measures of trajectories. The results yielded the robustness of CDTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80% accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.