Classifying Spatial Trajectories

TL;DR

This paper systematically compares various classifiers for spatial trajectory data, introduces new vectorization methods, and achieves state-of-the-art accuracy in transportation mode classification.

Contribution

It provides the first comprehensive comparison of trajectory classifiers, develops effective data-driven vectorization techniques, and sets new standards for trajectory classification.

Findings

New vectorization methods outperform existing techniques

Achieves state-of-the-art accuracy in transportation mode classification

Provides a comprehensive benchmark for trajectory classification methods

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.