# Urban Anomalies: A Simulated Human Mobility Dataset with Injected Anomalies

arXiv: https://arxiv.org/abs/2410.01844 · HTML: https://mlsanity.com/paper/2410.01844/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2024.

## Built on by (most cited first, 10 of 10)

- [Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework](https://mlsanity.com/paper/2410.01011/) (2024)
- [GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife Dataset](https://mlsanity.com/paper/2410.11853/) (2024)
- [Towards Physics-informed Diffusion for Anomaly Detection in Trajectories](https://mlsanity.com/paper/2506.06999/) (2025)
- [Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions](https://mlsanity.com/paper/2504.16358/) (2025)
- [GPS-MTM: Capturing Pattern of Normalcy in GPS-Trajectories with self-supervised learning](https://mlsanity.com/paper/2509.24031/) (2025)
- [Markovian Reeb Graphs for Simulating Spatiotemporal Patterns of Life](https://mlsanity.com/paper/2510.03152/) (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)
- [Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection](https://mlsanity.com/paper/2603.25255/) (2026)
- [TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond](https://mlsanity.com/paper/2605.06906/) (2026)
- [IBAD: Interpretable Behavioral Anomaly Detection on Human Mobility Data](https://mlsanity.com/paper/2606.16023/) (2026)

## Abstract (arXiv)

Human mobility anomaly detection based on location is essential in areas such as public health, safety, welfare, and urban planning. Developing models and approaches for location-based anomaly detection requires a comprehensive dataset. However, privacy concerns and the absence of ground truth hinder the availability of publicly available datasets. With this paper, we provide extensive simulated human mobility datasets featuring various anomaly types created using an existing Urban Patterns of Life Simulation. To create these datasets, we inject changes in the logic of individual agents to change their behavior. Specifically, we create four of anomalous agent behavior by (1) changing the agents' appetite (causing agents to have meals more frequently), (2) changing their group of interest (causing agents to interact with different agents from another group). (3) changing their social place selection (causing agents to visit different recreational places) and (4) changing their work schedule (causing agents to skip work), For each type of anomaly, we use three degrees of behavioral change to tune the difficulty of detecting the anomalous agents. To select agents to inject anomalous behavior into, we employ three methods: (1) Random selection using a centralized manipulation mechanism, (2) Spread based selection using an infectious disease model, and (3) through exposure of agents to a specific location. All datasets are split into normal and anomalous phases. The normal phase, which can be used for training models of normalcy, exhibits no anomalous behavior. The anomalous phase, which can be used for testing for anomalous detection algorithm, includes ground truth labels that indicate, for each five-minute simulation step, which agents are anomalous at that time. Datasets are generated using the maps (roads and buildings) for Atlanta and Berlin, having 1k agents in each simulation.