Classifying Spatial Trajectories

• URL: http://arxiv.org/abs/2209.01322v1
• Date: Sat, 3 Sep 2022 04:17:39 GMT
• Title: Classifying Spatial Trajectories
• Authors: Hasan Pourmahmood-Aghababa and Jeff M. Phillips
• Abstract summary: First comprehensive study on how to classify trajectories using only their spatial representations. New methods for how to vectorize trajectories via a data-driven method to select the associated landmarks prove among the most effective.
• Score: 13.200502573462712
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We provide the first comprehensive study on how to classify trajectories using only their spatial representations, measured on 5 real-world data sets. 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.

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