# The Patterns of Life Human Mobility Simulation

arXiv: https://arxiv.org/abs/2410.00185 · HTML: https://mlsanity.com/paper/2410.00185/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [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)
- [Human Mobility Datasets Enriched With Contextual and Social Dimensions](https://mlsanity.com/paper/2510.02333/) (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)
- [Staypoint Detection from Noisy Trajectory Data [Experiment Paper]](https://mlsanity.com/paper/2607.19312/) (2026)

## Abstract (arXiv)

We demonstrate the Patterns of Life Simulation to create realistic simulations of human mobility in a city. This simulation has recently been used to generate massive amounts of trajectory and check-in data. Our demonstration focuses on using the simulation twofold: (1) using the graphical user interface (GUI), and (2) running the simulation headless by disabling the GUI for faster data generation. We further demonstrate how the Patterns of Life simulation can be used to simulate any region on Earth by using publicly available data from OpenStreetMap. Finally, we also demonstrate recent improvements to the scalability of the simulation allows simulating up to 100,000 individual agents for years of simulation time. During our demonstration, as well as offline using our guides on GitHub, participants will learn: (1) The theories of human behavior driving the Patters of Life simulation, (2) how to simulate to generate massive amounts of synthetic yet realistic trajectory data, (3) running the simulation for a region of interest chosen by participants using OSM data, (4) learn the scalability of the simulation and understand the properties of generated data, and (5) manage thousands of parallel simulation instances running concurrently.