CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents
Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when prompted as agents, can generate lifelike daily routines at city scale. Yet these methods typically rely on few-shot prompting, causing agents to reproduce the LLM's behavioral priors rather than the target population. We introduce CityReal, a modular framework for human-aligned urban simulation. CityReal models agents as intention-driven decision makers that pursue coherent mobility and activity plans rather than isolated step-by-step choices. They adapt over time by learning habits and preferences based on experience and c...
| Authors | Nicolas Bougie, Xiaotong Ye, Narimasa Watanabe |
|---|---|
| Primary category | physics.soc-ph |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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