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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.03548 |
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| _version_ | 1866910986273816576 |
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| author | Ye, Chenglong Xiong, Gang Shang, Junyou Dai, Xingyuan Gong, Xiaoyan Lv, Yisheng |
| author_facet | Ye, Chenglong Xiong, Gang Shang, Junyou Dai, Xingyuan Gong, Xiaoyan Lv, Yisheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03548 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization Ye, Chenglong Xiong, Gang Shang, Junyou Dai, Xingyuan Gong, Xiaoyan Lv, Yisheng Artificial Intelligence 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. |
| title | SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.03548 |