用大模型智能体构建更真实的交通系统仿真框架。
Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework
- 用大语言模型构建可自主决策的交通参与者智能体。
- 在瓶颈场景中验证了智能体具备学习与行为调整能力。
- 适合关注智能交通、行为建模的研究者参考。
在交通需求建模与仿真领域，基于智能体的模型和微观仿真目前是主流方法。然而，现有基于智能体的模型在行为真实性和资源需求方面仍存在局限，影响其应用范围。本研究利用大语言模型（LLMs）及其衍生的智能体技术，提出一种通用的基于大模型智能体的交通系统建模框架。我们认为，大模型智能体不仅具备作为智能体的核心能力，还能有效解决现有模型的部分局限性。该概念框架紧密复现了交通网络中人类出行者的决策过程与交互特征，并通过相关研究及一个示范性案例，展示了所提系统在决策与学习行为方面满足关键行为标准的能力。尽管该框架仍需进一步优化，但我们认为此方法有望提升交通系统建模与仿真的水平。
原文摘要 · Abstract (English)
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.
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