  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.
