# BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data

arXiv: https://arxiv.org/abs/2510.12076 · HTML: https://mlsanity.com/paper/2510.12076/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://mlsanity.com/paper/2409.03024/) (2024)
- [DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis](https://mlsanity.com/paper/2003.01351/) (2020)
- [Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis](https://mlsanity.com/paper/2408.13082/) (2024)
- [Clustering Human Mobility with Multiple Spaces](https://mlsanity.com/paper/2301.08524/) (2023)
- [Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework](https://mlsanity.com/paper/2410.01011/) (2024)
- [Context-Aware Trajectory Anomaly Detection](https://mlsanity.com/paper/2410.19136/) (2024)

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

- [Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection](https://mlsanity.com/paper/2603.25255/) (2026)
- [TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation](https://mlsanity.com/paper/2606.12657/) (2026)

## Abstract (arXiv)

Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anomaly Detection for Human Mobility Data), an unsupervised framework that captures individualized behavioral signatures across large populations and uncovers fine-grained anomalies by jointly modeling spatial context and temporal dynamics. BeSTAD learns semantically enriched mobility representations that integrate location meaning and temporal patterns, enabling the detection of subtle deviations in individual movement behavior. BeSTAD further employs a behavior-cluster-aware modeling mechanism that builds personalized behavioral profiles from normal activity and identifies anomalies through cross-period behavioral comparison with consistent semantic alignment. Building on prior work in mobility behavior clustering, this approach enables not only the detection of behavioral shifts and deviations from established routines but also the identification of individuals exhibiting such changes within large-scale mobility datasets. By learning individual behaviors directly from unlabeled data, BeSTAD advances anomaly detection toward personalized and interpretable mobility analysis.