In transportation system demand modeling and simulation, agent-based models and microsimulations are current state-of-the-art approaches. However, existing agent-based models still have some limitations on behavioral realism and resource demand that limit their applicability. In this study, leveraging the emerging technology of large language models (LLMs) and LLM-based agents, we propose a general LLM-agent-based modeling framework for transportation systems. We argue that LLM agents not only possess the essential capabilities to function as agents but also offer promising solutions to overcome some limitations of existing agent-based models. Our conceptual framework design closely replicates the decision-making and interaction processes and traits of human travelers within transportation networks, and we demonstrate that the proposed systems can meet critical behavioral criteria for decision-making and learning behaviors using related studies and a demonstrative example of LLM agents' learning and adjustment in the bottleneck setting. Although further refinement of the LLM-agent-based modeling framework is necessary, we believe that this approach has the potential to improve transportation system modeling and simulation.

翻译：在交通系统需求建模与仿真领域，基于智能体的模型与微观仿真是当前最先进的方法。然而，现有的基于智能体的模型在行为真实性与资源需求方面仍存在一定局限性，制约了其应用范围。本研究借助新兴的大语言模型（LLMs）及基于LLM的智能体技术，提出了一个面向交通系统的通用型LLM智能体建模框架。我们认为，LLM智能体不仅具备作为智能体所需的核心能力，而且为解决现有基于智能体模型的某些局限性提供了可行方案。我们的概念框架设计紧密复现了交通网络中人类出行者的决策、交互过程与行为特征，并通过相关研究及一个演示性案例——展示LLM智能体在瓶颈场景中的学习与调整行为——论证了所提系统能够满足决策与学习行为的关键行为准则。尽管基于LLM智能体的建模框架仍需进一步完善，但我们相信该方法具有提升交通系统建模与仿真能力的潜力。