TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control
This work addresses usability issues in traffic simulation for non-expert users, though it appears incremental as it builds on existing simulators and LLM-based methods.
The authors tackled the challenge of making traffic simulation accessible to non-experts by proposing TrafficSimAgent, an LLM-based hierarchical agent framework that interprets natural language instructions and optimizes experiments, showing it effectively executes simulations and outperforms other systems and SOTA LLM methods.
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.