# Classifying Spatial Trajectories

arXiv: https://arxiv.org/abs/2209.01322 · HTML: https://mlsanity.com/paper/2209.01322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 3 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [Clustering time series under the Fr'echet distance](https://mlsanity.com/paper/1512.04349/) (2015)
- [Simple Distances for Trajectories via Landmarks](https://mlsanity.com/paper/1804.11284/) (2018)
- [Approximating (k,ℓ)-center clustering for curves](https://mlsanity.com/paper/1805.01547/) (2018)
- [Orientation-Preserving Vectorized Distance Between Curves](https://mlsanity.com/paper/2007.15924/) (2020)

## Built on by (most cited first, 1 of 1)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)

## Abstract (arXiv)

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.