用大模型模拟城市居民行为，让虚拟人更像真人。
CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation
- 用大模型生成有目标、习惯和情境判断的每日行程
- 支持长期记忆与导航，模拟出真实人群流动模式
- 适合城市规划、社会研究者用于预测人流与场所热度
建模城市环境中的人类行为对社会科学、行为研究和城市规划至关重要。以往方法多依赖固定规则，难以模拟复杂意图、计划和自适应行为。为此，我们提出城市仿真系统 CitySim，利用大语言模型在人类级智能上的突破。在 CitySim 中，代理通过递归的价值驱动方式生成真实日常日程，平衡必要活动、个人习惯与情境因素。为实现长期、逼真的模拟，代理被赋予信念、长期目标及空间记忆以进行导航。CitySim 在微观与宏观层面均比以往方法更贴近真实人类行为。我们还通过模拟数万名代理，在多种现实场景中评估其集体行为，包括估算人群密度、预测场所受欢迎程度以及评估福祉水平。结果表明，CitySim 是一个可扩展、灵活的城市现象理解与预测测试平台。
原文摘要 · Abstract (English)
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.
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