# TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control

arXiv: https://arxiv.org/abs/2512.20996 · HTML: https://mlsanity.com/paper/2512.20996/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 15 of 15)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [AutoWebGLM: A Large Language Model-based Web Navigating Agent](https://mlsanity.com/paper/2404.03648/) (2024)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023)
- [Open-TI: Open Traffic Intelligence with Augmented Language Model](https://mlsanity.com/paper/2401.00211/) (2023)
- [ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility](https://mlsanity.com/paper/2409.09040/) (2024)
- [Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept](https://mlsanity.com/paper/2402.12682/) (2024)
- [AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents](https://mlsanity.com/paper/2505.10321/) (2025)
- [RoboCar: A Rapidly Deployable Open-Source Platform for Autonomous Driving Research](https://mlsanity.com/paper/2405.03572/) (2024)
- [MOSS: A Large-scale Open Microscopic Traffic Simulation System](https://mlsanity.com/paper/2405.12520/) (2024)
- [LCSim: A Large-Scale Controllable Traffic Simulator](https://mlsanity.com/paper/2406.19781/) (2024)
- [A Max Pressure Algorithm for Traffic Signals Considering Pedestrian Queues](https://mlsanity.com/paper/2406.19305/) (2024)

## Built on by (most cited first, 3 of 3)

- [SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation](https://mlsanity.com/paper/2609.03753/) (2026)
- [Decoupled Intelligence: A Multi-Agent LLM Framework for Controllable Traffic Scenario Generation in SUMO](https://mlsanity.com/paper/2605.27685/) (2026)
- [SimTIO: A Simulation-Grounded Multi-Agent LLM Framework for Compositional Traffic Intervention Optimization](https://mlsanity.com/paper/2609.05740/) (2026)

## Abstract (arXiv)

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.