用大模型模拟真实人类移动轨迹，兼顾隐私与可解释性。
TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation
- 分层架构融合人格生成、活动选择与目的地预测
- 模拟轨迹与真实数据模式高度一致，支持城市规划等应用
- 可动态生成个性化日常行为，适合社会科学研究
本文利用大语言模型（LLMs）模拟人类移动行为，解决传统模型成本高、隐私风险大的问题。提出分层框架，整合人格生成、活动选择与目的地预测模块，结合真实人口与心理数据生成逼真移动模式。通过摘要与加权密度指标结构化数据，实现可扩展的内存管理并保留关键洞察。初步结果表明，基于LLM的模拟轨迹与真实世界模式吻合良好，为城市规划、交通管理与公共卫生等社会问题提供可扩展、可解释的分析支持。框架具备动态生成人格与活动的能力，可灵活适应不同场景。代码与交互演示已开源于https://github.com/cju0/TrajLLM。
原文摘要 · Abstract (English)
This work leverages Large Language Models (LLMs) to simulate human mobility, addressing challenges like high costs and privacy concerns in traditional models. Our hierarchical framework integrates persona generation, activity selection, and destination prediction, using real-world demographic and psychological data to create realistic movement patterns. Both physical models and language models are employed to explore and demonstrate different methodologies for human mobility simulation. By structuring data with summarization and weighted density metrics, the system ensures scalable memory management while retaining actionable insights. Preliminary results indicate that LLM-driven simulations align with observed real-world patterns, offering scalable, interpretable insights for social problems such as urban planning, traffic management, and public health. The framework's ability to dynamically generate personas and activities enables it to provide adaptable and realistic daily routines. This study demonstrates the transformative potential of LLMs in advancing mobility modeling for societal and urban applications. The source code and interactive demo for our framework are available at https://github.com/cju0/TrajLLM.
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