This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and efficient personal mobility generation. LLMs overcome the limitations of previous models by efficiently processing semantic data and offering versatility in modeling various tasks. Our approach addresses the critical need to align LLMs with real-world urban mobility data, focusing on three research questions: aligning LLMs with rich activity data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. In experimental studies, comprehensive validation is performed using real-world data. This research marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis.

翻译：本文提出了一种新颖方法，将大型语言模型（LLMs）集成到智能体框架中，用于灵活高效的个人移动性生成。LLMs通过高效处理语义数据并在多种任务建模中展现多功能性，克服了以往模型的局限性。我们的方法解决了LLMs与现实城市移动数据对齐的关键需求，聚焦于三个研究问题：将LLMs与丰富的活动数据对齐、开发可靠的活动生成策略、以及探索LLMs在城市移动中的应用。关键技术贡献在于提出了一种新颖的LLM智能体框架，该框架考虑了个人活动模式与动机，包括一种用于将LLMs与现实活动数据对齐的自一致性方法，以及一种用于可解释活动生成的检索增强策略。在实验研究中，我们使用真实数据进行了全面验证。这项研究开创性地设计了基于现实人类活动数据进行活动生成的LLM智能体框架，为城市移动性分析提供了有前景的工具。