An experimental study on classifying spatial trajectories
An experimental study on classifying spatial trajectories
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DOI：
10.1007/s10115-022-01802-5
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发表时间：
2022-12
影响因子：
2.7
通讯作者：
H. Pourmahmood-Aghababa;J. M. Phillips
中科院分区：
文献类型：
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作者：
H. Pourmahmood-Aghababa;J. M. Phillips
我们提供了第一个全面的研究如何分类轨迹只使用他们的空间表示，测量5个真实世界的数据集。我们的比较考虑20个不同的分类器，无论是作为一个流行的距离的KNN分类器，或作为一个更一般类型的分类器，使用每个轨迹的矢量化表示。我们还开发了新的方法，用于如何通过数据驱动的方法来选择相关的地标来矢量化轨迹，这些方法在我们的研究中被证明是最有效的。这些矢量化方法使用起来简单有效，并且还在已建立的运输模式分类任务上提供了最先进的准确性。总之，这项研究为如何对轨迹进行分类设定了标准，包括引入新的简单技术来实现这些结果，并为未来不可避免的关于这一主题的研究设定了严格的标准。
We provide the first comprehensive study on how to classify trajectories using only their spatial representations, measured on 5 real-world datasets. Our comparison considers 20 distinct classifiers arising either as a KNN classifier of a popular distance, or as a more general type of classifier using a vectorized representation of each trajectory. We additionally develop new methods for how to vectorize trajectories via a data-driven method to select the associated landmarks, and these methods prove among the most effective in our study. These vectorized approaches are simple and efficient to use, and also provide state-of-the-art accuracy on an established transportation mode classification task. In all, this study sets the standard for how to classify trajectories, including introducing new simple techniques to achieve these results, and sets a rigorous standard for the inevitable future study on this topic.