SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization (2506.03548v1)

Abstract: 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.

• The paper presents a novel SUMO-MCP platform that integrates the Model Context Protocol with SUMO, transforming complex simulation workflows into user-friendly, natural language-driven processes.
• It automates critical tasks such as network setup, traffic-demand generation, and simulation execution, significantly reducing the technical barriers for non-experts.
• Experimental results demonstrate notable reductions in travel times and delays, proving the platform’s effectiveness in optimizing traffic signal timings and improving urban mobility.

An Overview of SUMO-MCP: Enhancing Traffic Simulation and Optimization through Model Context Protocol

The paper "SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization" presents a significant advancement in the field of urban mobility research by integrating LLMs with the Simulation of Urban Mobility (SUMO) software. The authors introduce a new platform, SUMO-MCP, which aims to simplify and streamline traffic simulation processes, rendering them more accessible to users who may not possess extensive technical expertise in traffic management or simulation coding.

Key Contributions

The paper's primary contribution is the development and implementation of the SUMO-MCP platform, which incorporates the Model Context Protocol (MCP) to wrap SUMO's core utilities into a cohesive tool suite. This integration allows for dynamic and flexible workflows, driven by natural-language prompts, effectively transforming SUMO into a more user-friendly, "chat-ready" tool. Notably, SUMO-MCP addresses the complexity of SUMO's traditional interface, particularly the Traffic Control Interface (TraCI), thus reducing the technical barriers for non-experts.

The SUMO-MCP platform supports a range of functionalities, including:

• Network Handling: Automatic network downloading and conversion from OpenStreetMap, facilitating the initial setup of traffic scenarios.
• Traffic-Demand Generation: Users can create vehicle demands from origin-destination matrices or random patterns.
• Simulation Execution: The platform enables the execution of batch simulations using various signal-control strategies, such as Fixed-Time, Actuated, Webster, and GreenWave.
• Optimization and Analysis: It can detect traffic congestion and optimize signal timings accordingly, enhancing the efficiency of traffic signal control.

Methodology and Experimental Evaluation

The SUMO-MCP framework utilizes a client-server architecture, where the client is an LLM-based agent that interprets user requests into MCP calls. The server, built on FastMCP, dynamically registers the required tools using lightweight JSON-RPC messages, handling the underlying complexity of simulation configuration and execution.

To evaluate SUMO-MCP's effectiveness, the authors conducted several experiments, demonstrating its capability to fully automate complex simulation workflows from natural-language prompts. The platform showed notable performance improvements at key intersections within simulated networks, significantly reducing average travel times and delays compared to baseline scenarios without optimization. These results underscore the platform's potential to drastically reduce setup times, minimize errors, and streamline traffic management processes.

Moreover, the authors highlight the advantages of using dynamic import within the framework to minimize memory usage and reduce tool overload. The incorporation of MCP further enhances the usability and robustness of the platform, suggesting that it has practical implications for a broad range of traffic simulation and control applications.

Implications and Future Directions

The introduction of SUMO-MCP embodies a leap forward in the automation and accessibility of traffic simulation and optimization. By simplifying the interaction with SUMO via natural-language processing and integrating adaptive optimization techniques, SUMO-MCP offers a promising tool for traffic engineers, urban planners, and researchers aiming to develop smarter and more efficient urban mobility solutions.

Looking forward, the authors propose extending MCP to allow dynamic registration and discovery of file-based resources generated during simulations. Additionally, there is potential to explore more advanced planning strategies and develop user-friendly interfaces to promote interactive experiment design.

Overall, the paper delivers compelling insights into how contemporary AI frameworks can be leveraged to enhance traditional traffic simulation tools, paving the way for further advancements in the intersection of artificial intelligence and intelligent transportation systems.