用自然语言一键生成交通仿真，自动优化信号灯
SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization
- 通过自然语言指令调用SUMO工具链，自动化处理数据与仿真
- 支持批量运行多种信号控制策略并自动生成对比报告
- 无需编码即可灵活组合流程，适合交通研究新手
交通仿真工具如SUMO在城市交通研究中至关重要，但其复杂的手动流程——包括网络下载、需求生成、仿真配置和结果分析——给用户带来挑战。本文提出SUMO-MCP，一个将SUMO核心功能整合为统一工具套件的新平台，并提供常见预处理与后处理辅助功能。用户可通过简单自然语言提示，从OpenStreetMap数据生成交通场景，基于起讫矩阵或随机模式生成交通需求，批量运行多种信号控制策略，自动完成对比分析与报告生成，并检测拥堵以优化信号配时。平台支持动态组合暴露的SUMO工具，实现灵活自定义工作流，无需额外编码。实验表明，SUMO-MCP显著提升了交通仿真的可访问性与可靠性。代码将于未来开源于https://github.com/ycycycl/SUMO-MCP。
原文摘要 · Abstract (English)
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.
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。