CityReal framework uses LLMs to simulate realistic urban human behavior

Researchers have developed CityReal, a new framework for simulating urban dynamics and human behavior using large language models (LLMs). Unlike previous methods that relied on limited prompting, CityReal models agents as intention-driven decision-makers who learn habits and preferences over time. This approach aligns agent decisions with observed population statistics, leading to more realistic micro and macro-level human behavior simulations. The framework can scale to tens of thousands of agents, enabling analysis of crowd density, mobility, and well-being under various urban scenarios. AI

IMPACT Enhances urban planning and policy analysis by providing a more realistic simulation of human behavior and city dynamics.

RANK_REASON The cluster describes a new research paper detailing a novel framework for urban simulation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• CityReal
• human behavior
• large-language models
• mobility
• road safety
• social science
• urban simulation

• paper
• product

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