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The Patterns of Life Human Mobility Simulation

Hossein Amiri, Will Kohn, Shiyang Ruan, Joon-Seok Kim, Hamdi Kavak, Andrew Crooks, Dieter Pfoser, Carola Wenk, Andreas Zufle

TL;DR

This paper addresses the challenge of obtaining large-scale, realistic human mobility data by presenting the Patterns of Life Simulation (POL), an agent-based model implemented in Java/MASON that is guided by Maslowian needs to drive agents' behavior. It demonstrates both interactive GUI and headless data-generation modes, and shows how to adapt POL to any geographic region using OpenStreetMap data, including a three-shapefile workflow and region-specific map construction. Significant contributions include a scalable, parallelizable framework for generating massive synthetic trajectory and check-in datasets, performance optimizations that enable simulations with hundreds of thousands of agents, and open-source tooling for region customization and data processing. The work enables rapid, reproducible generation of synthetic mobility data for urban planning, epidemiology, and transportation research, potentially reducing reliance on real-world data collection while preserving realism and study-scale.

Abstract

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.

The Patterns of Life Human Mobility Simulation

TL;DR

This paper addresses the challenge of obtaining large-scale, realistic human mobility data by presenting the Patterns of Life Simulation (POL), an agent-based model implemented in Java/MASON that is guided by Maslowian needs to drive agents' behavior. It demonstrates both interactive GUI and headless data-generation modes, and shows how to adapt POL to any geographic region using OpenStreetMap data, including a three-shapefile workflow and region-specific map construction. Significant contributions include a scalable, parallelizable framework for generating massive synthetic trajectory and check-in datasets, performance optimizations that enable simulations with hundreds of thousands of agents, and open-source tooling for region customization and data processing. The work enables rapid, reproducible generation of synthetic mobility data for urban planning, epidemiology, and transportation research, potentially reducing reliance on real-world data collection while preserving realism and study-scale.

Abstract

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.
Paper Structure (8 sections, 4 figures, 1 table)

This paper contains 8 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Demonstration
  3. Patterns of Life (POL) Demonstration: GUI
  4. POL Demonstration: Headless
  5. Adapting POL to Simulate other Region
  6. Large-Scale Data Generation
  7. Parallelization of POL
  8. Conclusion

Figures (4)

  • Figure 1: A screenshot of the graphical user interface of the Patterns of Life Simulation. The GUI shows the map and the movements of agents on the left side and the social network of agents and their statistical properties on the right side.
  • Figure 2: Steps to generate the one trajectory dataset.
  • Figure 3: Example visualization of the generated dataset for San Francisco, California, USA
  • Figure 4: Steps to generate a new map for the simulation.