Towards Universal Urban Patterns-of-Life Simulation

TL;DR

This paper introduces a scalable, agent-based model for simulating urban mobility patterns that accurately reflect empirical data across large metropolitan areas without city-specific calibration.

Contribution

The authors develop a universal, scalable framework for urban mobility simulation that captures diverse activity patterns and is validated against national survey data.

Findings

Model achieves over 0.80 similarity scores with empirical data.

Scales efficiently to over 20 million agents.

Enables scenario analysis for urban planning and disaster response.

Abstract

Understanding urban mobility requires models that capture how people interact with and navigate the built environment. We present a scalable, generalizable agent-based framework in which daily schedules emerge from the interplay between mandatory (e.g., work, school) and flexible (e.g., errands, food, leisure) activities, driven by evolving individual needs. The results of our model are validated against empirical patterns from the 2017 U.S. National Household Travel Survey, including activity distributions, origin-destination flows, and trip-chain length distributions. We introduce a normalized similarity metric to quantify agreement between simulated and empirical patterns. Most cities achieve scores above 0.80, demonstrating strong alignment without the need for city-specific calibration. The model scales efficiently to over 20 million agents, enabling full-population simulations of large metropolitan areas. This combination of universality and scalability enables scenario analysis for infrastructure stress testing, disaster recovery, innovation diffusion, and disease spread in urban systems.