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Clustering Trajectories by Relevant Parts for Air Traffic Analysis

机译:按相关部分对轨迹进行聚类以进行空中交通分析

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Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.
机译:通过相似度对运动对象的轨迹进行聚类是运动分析中的一项重要技术。现有的距离函数基于轨迹点或线段的属性来评估轨迹之间的相似性。这些属性可以包括空间位置,时间和主题属性。可能需要将分析重点放在轨迹的某些部分上,即具有特定属性的点和线段。根据分析重点,分析人员可能仅需要通过轨迹相关部分的相似性对其进行聚类。在整个分析过程中,焦点可能会更改,轨迹的不同部分可能会变得相关。我们提出了一种分析工作流,其中使用交互式过滤工具将相关性标志附加到轨迹元素上,使用忽略不相关元素的距离函数完成聚类,并对得到的聚类进行汇总以进行进一步分析。我们用来自空中交通领域的真实数据,在三个案例研究中演示了该工作流程如何对不同的分析任务有用​​。我们提出了一套通用技术和可视化指南,以通过相关性轨迹聚类来支持运动数据分析。

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