This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility 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. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study 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：本文提出了一种将大型语言模型（LLMs）集成到智能体框架中的新方法，用于灵活且有效的个人移动性生成。LLMs通过有效处理语义数据并在建模各类任务时提供多功能性，克服了先前模型的局限性。我们的方法解决了三个研究问题：使LLMs与现实世界城市移动数据对齐、开发可靠的活动生成策略，以及探索LLM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，该框架考虑了个体活动模式与动机，包括一种使LLMs与现实世界活动数据对齐的自洽方法，以及一种用于可解释活动生成的检索增强策略。我们评估了所提出的LLM智能体框架，并与最先进的个人移动性生成方法进行了比较，证明了该方法的有效性及其在城市移动中的潜在应用前景。总体而言，本研究开创了基于现实世界人类活动数据设计用于活动生成的LLM智能体框架的先河，为城市移动分析提供了一个前景广阔的工具。