用大模型模拟城市出行，让每个虚拟人的行为更真实。
Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation
- 用大语言模型生成个性化出行者数据，替代传统规则建模
- 在台北市仿真出真实的大规模出行模式与路线热力图
- 适合城市规划与交通政策制定者参考
本研究提出一种创新的城市交通仿真方法，将大语言模型（LLM）与基于代理的建模（ABM）相结合。不同于传统基于规则的ABM，该框架利用LLM生成合成人口特征、分配日常及偶发活动地点，并模拟个性化出行路径。基于真实世界数据，该仿真在台北市实现了个体行为与大规模出行模式的建模。关键成果包括路线热力图和分出行方式的指标，为城市规划者提供可操作的决策支持。未来工作将聚焦于建立可靠的验证框架，以确保在城市规划应用中的准确性与可靠性。
原文摘要 · Abstract (English)
This study presents an innovative approach to urban mobility simulation by integrating a Large Language Model (LLM) with Agent-Based Modeling (ABM). Unlike traditional rule-based ABM, the proposed framework leverages LLM to enhance agent diversity and realism by generating synthetic population profiles, allocating routine and occasional locations, and simulating personalized routes. Using real-world data, the simulation models individual behaviors and large-scale mobility patterns in Taipei City. Key insights, such as route heat maps and mode-specific indicators, provide urban planners with actionable information for policy-making. Future work focuses on establishing robust validation frameworks to ensure accuracy and reliability in urban planning applications.
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