CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation

TL;DR

CitySim leverages large language models to create a scalable, realistic agent-based urban simulation platform that captures nuanced human behaviors and collective city dynamics for planning and social science research.

Contribution

This work introduces CitySim, a novel large-scale agent simulation framework utilizing LLMs to model complex urban behaviors and long-term city dynamics.

Findings

CitySim produces more realistic agent behaviors than prior models.

The simulator accurately predicts crowd density and place popularity.

CitySim demonstrates scalability to tens of thousands of agents.

Abstract

Modeling human behavior in urban environments is fundamental for social science, behavioral studies, and urban planning. Prior work often rely on rigid, hand-crafted rules, limiting their ability to simulate nuanced intentions, plans, and adaptive behaviors. Addressing these challenges, we envision an urban simulator (CitySim), capitalizing on breakthroughs in human-level intelligence exhibited by large language models. In CitySim, agents generate realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. To enable long-term, lifelike simulations, we endow agents with beliefs, long-term goals, and spatial memory for navigation. CitySim exhibits closer alignment with real humans than prior work, both at micro and macro levels. Additionally, we conduct insightful experiments by modeling tens of thousands of agents and evaluating their collective behaviors under various real-world scenarios, including estimating crowd density, predicting place popularity, and assessing well-being. Our results highlight CitySim as a scalable, flexible testbed for understanding and forecasting urban phenomena.