TrajLLM：一种用于真实人类轨迹模拟的模块化LLM增强智能体框架
TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation
AI总结：
该研究提出TrajLLM，一种模块化LLM增强智能体框架，通过分层整合角色生成、活动选择和目的地预测，利用真实人口统计和心理数据模拟人类移动，提供可扩展且可解释的见解，以支持城市规划、交通管理和公共卫生等应用。
摘要：
本研究利用大语言模型（LLM）模拟人类移动性，以应对传统模型中成本高昂和隐私问题等挑战。我们的分层框架整合了角色生成、活动选择和目的地预测，利用真实世界的人口统计和心理数据来创建逼真的移动模式。我们同时采用物理模型和语言模型来探索和展示人类移动性模拟的不同方法。通过使用摘要和加权密度指标对数据进行结构化，系统确保了可扩展的内存管理，同时保留可操作的见解。初步结果表明，LLM驱动的模拟与观察到的现实世界模式相符，为城市规划、交通管理和公共卫生等社会问题提供了可扩展、可解释的见解。该框架动态生成角色和活动的能力使其能够提供适应性强且逼真的日常作息。本研究展示了LLM在推进社会和城市应用移动性建模方面的变革潜力。我们框架的源代码和交互式演示可在 https://github.com/cju0/TrajLLM 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
