用AI代理自动设计交通仿真实验，降低使用门槛。
TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control
- 分层代理协作：高层理解指令，底层实时决策
- 多场景测试均生成合理结果，模糊指令也能应对
- 自主优化性能优于现有系统和顶尖LLM方法
交通仿真对交通优化与政策制定至关重要。尽管SUMO和MATSim等现有模拟器功能完备，但普通用户在从零开始开展实验时仍面临巨大挑战。为此，我们提出TrafficSimAgent，一个基于大语言模型的代理框架，作为通用交通仿真任务中的实验设计与决策优化专家。该框架通过跨层级代理协作实现高效执行：高层专家代理以高灵活性理解自然语言指令，规划整体实验流程，并按需调用MCP兼容工具；低层专家代理则基于实时交通状况为基本元素选择最优行动方案。多场景实验证明，TrafficSimAgent能在各种条件下有效执行仿真，即使用户指令模糊也能持续产出合理结果。此外，精心设计的专家级自主决策优化机制使其性能显著优于其他系统及当前最先进的基于LLM的方法。
原文摘要 · Abstract (English)
Traffic simulation is important for transportation optimization and policy making. While existing simulators such as SUMO and MATSim offer fully-featured platforms and utilities, users without too much knowledge about these platforms often face significant challenges when conducting experiments from scratch and applying them to their daily work. To solve this challenge, we propose TrafficSimAgent, an LLM-based agent framework that serves as an expert in experiment design and decision optimization for general-purpose traffic simulation tasks. The framework facilitates execution through cross-level collaboration among expert agents: high-level expert agents comprehend natural language instructions with high flexibility, plan the overall experiment workflow, and invoke corresponding MCP-compatible tools on demand; meanwhile, low-level expert agents select optimal action plans for fundamental elements based on real-time traffic conditions. Extensive experiments across multiple scenarios show that TrafficSimAgent effectively executes simulations under various conditions and consistently produces reasonable outcomes even when user instructions are ambiguous. Besides, the carefully designed expert-level autonomous decision-driven optimization in TrafficSimAgent yields superior performance when compared with other systems and SOTA LLM based methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。