# Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories

arXiv: https://arxiv.org/abs/2410.00054 · HTML: https://mlsanity.com/paper/2410.00054/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 28 Sep 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Large Language Models for Spatial Trajectory Patterns Mining](https://mlsanity.com/paper/2310.04942/) (2023)
- [Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories](https://mlsanity.com/paper/2409.18427/) (2024)
- [Contact Tracing: Beyond the Apps](https://mlsanity.com/paper/2006.04585/) (2020)

## Built on by (most cited first, 5 of 5)

- [GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife Dataset](https://mlsanity.com/paper/2410.11853/) (2024)
- [POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution](https://mlsanity.com/paper/2507.09137/) (2025)
- [World-POI: Global Point-of-Interest Data Enriched from Foursquare and OpenStreetMap as Tabular and Graph Data](https://mlsanity.com/paper/2510.21342/) (2025)
- [IBAD: Interpretable Behavioral Anomaly Detection on Human Mobility Data](https://mlsanity.com/paper/2606.16023/) (2026)
- [Staypoint Detection from Noisy Trajectory Data [Experiment Paper]](https://mlsanity.com/paper/2607.19312/) (2026)

## Abstract (arXiv)

Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge and limits its ability to identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal data across spatial, temporal, and textual dimensions. Addressing the need for a domain-agnostic model, we propose the Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework.TOD4Traj first introduces a modality feature unification module to align diverse data feature representations, enabling the integration of multi-modal information and enhancing transferability across different datasets. A contrastive learning module is further pro-posed for identifying regular mobility patterns both temporally and across populations, allowing for a joint detection of outliers based on individual consistency and group majority patterns. Our experimental results have shown TOD4Traj's superior performance over existing models, demonstrating its effectiveness and adaptability in detecting human trajectory outliers across various datasets.