Classifying Spatial Trajectories
This work provides a comprehensive benchmark for trajectory classification, addressing a domain-specific problem in spatial data analysis with incremental methodological improvements.
The authors tackled the problem of classifying spatial trajectories using only spatial representations, comparing 20 classifiers across 5 real-world datasets and developing new vectorization methods with data-driven landmark selection. Their vectorized approaches achieved state-of-the-art accuracy on a transportation mode classification task, setting a rigorous standard for future studies.
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.