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

TL;DR

GraphTrafficGPT introduces a graph-based AI architecture for traffic management, enabling efficient task coordination, reduced token use, and faster responses compared to previous chain-based systems.

Contribution

It presents a novel graph-structured framework for LLM-driven traffic management, improving scalability and efficiency over existing chain-based approaches.

Findings

Reduces token consumption by 50.2%

Decreases response latency by 19.0%

Supports multi-query processing with 23.0% efficiency gain

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.