GraphTrafficGPT: Enhancing Traffic Management Through Graph-Based AI Agent Coordination

• URL: http://arxiv.org/abs/2507.13511v1
• Date: Thu, 17 Jul 2025 19:41:09 GMT
• Title: GraphTrafficGPT: Enhancing Traffic Management Through Graph-Based AI Agent Coordination
• Authors: Nabil Abdelaziz Ferhat Taleb, Abdolazim Rezaei, Raj Atulkumar Patel, Mehdi Sookhak,
• Abstract summary: GraphTrafficGPT represents tasks and dependencies as nodes and edges in a directed graph, enabling efficient parallel execution and dynamic resource allocation.<n>Brain Agent decomposes user queries, constructs optimized dependency graphs, and coordinates a network of specialized agents for data retrieval, analysis, visualization, and simulation.<n> Experimental results demonstrate that GraphTrafficGPT reduces token consumption by 50.2% and average response latency by 19.0% compared to TrafficGPT.
• Score: 0.8999666725996978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) offer significant promise for intelligent traffic management; however, current chain-based systems like TrafficGPT are hindered by sequential task execution, high token usage, and poor scalability, making them inefficient for complex, real-world scenarios. To address these limitations, we propose GraphTrafficGPT, a novel graph-based architecture, which fundamentally redesigns the task coordination process for LLM-driven traffic applications. GraphTrafficGPT represents tasks and their dependencies as nodes and edges in a directed graph, enabling efficient parallel execution and dynamic resource allocation. The main idea behind the proposed model is a Brain Agent that decomposes user queries, constructs optimized dependency graphs, and coordinates a network of specialized agents for data retrieval, analysis, visualization, and simulation. By introducing advanced context-aware token management and supporting concurrent multi-query processing, the proposed architecture handles interdependent tasks typical of modern urban mobility environments. Experimental results demonstrate that GraphTrafficGPT reduces token consumption by 50.2% and average response latency by 19.0% compared to TrafficGPT, while supporting simultaneous multi-query execution with up to 23.0% improvement in efficiency.

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