Large Language Models as Urban Residents: An LLM Agent Framework for
  Personal Mobility Generation
Jiawei Wang Renhe Jiang Chuang Yang Zengqing Wu Makoto Onizuka Ryosuke Shibasaki Chuan Xiao
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.