Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation Jiawei Wang 1 Chuang Yang 1 Zengqing Wu

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.