Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation

arXiv:2402.14744v1 Announce Type: cross
Abstract: 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 …

abstract agent arxiv cs.ai cs.cl cs.cy cs.lg data framework language language models large language large language models limitations llm llms mobility modeling novel paper processing semantic type urban