# Kinematic Detection of Anomalies in Human Trajectory Data

arXiv: https://arxiv.org/abs/2409.19136 · HTML: https://mlsanity.com/paper/2409.19136/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Large Language Models for Spatial Trajectory Patterns Mining](https://mlsanity.com/paper/2310.04942/) (2023)
- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://mlsanity.com/paper/2409.03024/) (2024)

## Built on by (most cited first, 2 of 2)

- [Towards Physics-informed Diffusion for Anomaly Detection in Trajectories](https://mlsanity.com/paper/2506.06999/) (2025)
- [Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection](https://mlsanity.com/paper/2603.25255/) (2026)

## Abstract (arXiv)

Historically, much of the research in understanding, modeling, and mining human trajectory data has focused on where an individual stays. Thus, the focus of existing research has been on where a user goes. On the other hand, the study of how a user moves between locations has great potential for new research opportunities. Kinematic features describe how an individual moves between locations and can be used for tasks such as identification of individuals or anomaly detection. Unfortunately, data availability and quality challenges make kinematic trajectory mining difficult. In this paper, we leverage the Geolife dataset of human trajectories to investigate the viability of using kinematic features to identify individuals and detect anomalies. We show that humans have an individual "kinematic profile" which can be used as a strong signal to identify individual humans. We experimentally show that, for the two use-cases of individual identification and anomaly detection, simple kinematic features fed to standard classification and anomaly detection algorithms significantly improve results.