mlsanityData to 14 Oct 2026

SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization

arXiv 2506.03548 ↗First posted 4 Jun 2025cs.AIcited by 3

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Abstract (arXiv)

Traffic simulation tools, such as SUMO, are essential for urban mobility research. However, such tools remain challenging for users due to complex manual workflows involving network download, demand generation, simulation setup, and result analysis. In this paper, we introduce SUMO-MCP, a novel platform that not only wraps SUMO' s core utilities into a unified tool suite but also provides additional auxiliary utilities for common preprocessing and postprocessing tasks. Using SUMO-MCP, users can issue simple natural-language prompts to generate traffic scenarios from OpenStreetMap data, create demand from origin-destination matrices or random patterns, run batch simulations with multiple signal-control strategies, perform comparative analyses with automated reporting, and detect congestion for signal-timing optimization. Furthermore, the platform allows flexible custom workflows by dynamically combining exposed SUMO tools without additional coding. Experiments demonstrate that SUMO-MCP significantly makes traffic simulation more accessible and reliable for researchers. We will release code for SUMO-MCP at https://github.com/ycycycl/SUMO-MCP in the future.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 4 Jun 2025.

Builds on

13 of 39 references.

References · 13

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  3. 2023ChatDev: Communicative Agents for Software Developmentcited by 919
  4. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  5. 2023LLMLight: Large Language Models as Traffic Signal Control Agentscited by 73
  6. 2023Open-TI: Open Traffic Intelligence with Augmented Language Modelcited by 38
  7. 2024Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAIcited by 35
  8. 2024ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObilitycited by 34
  9. 2024LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environmentscited by 31
  10. 2024Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Conceptcited by 15
  11. 2025CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Controlcited by 15
  12. 2024RoboCar: A Rapidly Deployable Open-Source Platform for Autonomous Driving Researchcited by 6
1 more
  1. 2024Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuationscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Modelscited by 3
  2. 2026SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulationcited by 1
  3. 2026Decoupled Intelligence: A Multi-Agent LLM Framework for Controllable Traffic Scenario Generation in SUMOcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control
  2. 2025Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control
  3. 2025Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks
  4. 2024TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles
  5. 2025Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator
  6. 2025DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models
  7. 2021Scalable Multiagent Driving Policies For Reducing Traffic Congestion
  8. 2021Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction