GraphTrafficGPT: Enhancing Traffic Management Through Graph-Based AI Agent Coordination (2507.13511v1)

Abstract: 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.

• The paper introduces a graph-based architecture that transforms sequential task processing into parallel execution, reducing token consumption by up to 60% and latency by 19%.
• It employs a Brain Agent to decompose queries into independent tasks managed by specialized ReAct agents via an efficient Multi-Agent Communication Protocol.
• Experimental results demonstrate efficiency gains, including a 37.6% improvement in multi-query processing and a 61.5% reduction in operational costs.

GraphTrafficGPT: A Graph-Based Paradigm for Scalable LLM-Driven Traffic Management

GraphTrafficGPT introduces a graph-based multi-agent architecture for LLM-powered traffic management, addressing the scalability, efficiency, and cost limitations inherent in prior chain-based systems such as TrafficGPT. The paper presents a comprehensive system design, empirical evaluation, and a discussion of practical implications for real-world deployment in urban mobility environments.

Architectural Innovations

The core innovation of GraphTrafficGPT is the transformation of sequential, chain-based task processing into a directed dependency graph. Each node in this graph represents a discrete traffic management task (e.g., data retrieval, analysis, visualization, simulation), and edges encode inter-task dependencies. This structure enables the system to:

• Identify and execute independent tasks in parallel, maximizing resource utilization and reducing overall latency.
• Dynamically allocate computational resources based on task complexity and priority.
• Support concurrent multi-query processing, a critical requirement for operational traffic management centers.

Central to the architecture is the Brain Agent, which performs query decomposition, dependency analysis, and orchestrates a network of specialized agents. These agents—each equipped with a ReAct (Reasoning and Action) loop—handle domain-specific subtasks and interface with Traffic Foundation Models (TFMs). The Multi-Agent Communication Protocol (MCP) ensures efficient, asynchronous information exchange and context sharing across agents.

Algorithmic Workflow

The system workflow is formalized as follows:

1. Query Decomposition: The Brain Agent parses user queries into atomic tasks.
2. Dependency Graph Construction: Tasks and their dependencies are represented as a directed graph.
3. Parallel Task Assignment: Independent tasks are dispatched to specialized agents for concurrent execution.
4. Context-Aware Token Management: Redundant context is pruned, and relevant information is selectively propagated.
5. Result Integration: Outputs from agents are synthesized into a coherent response.

This approach is encapsulated in the provided pseudocode, which highlights the parallel execution of independent tasks and the dynamic selection of agents based on task type.

Empirical Results

The experimental evaluation demonstrates strong numerical improvements over the original TrafficGPT:

• Token Consumption: Average reduction of 50.2%, with some functions exceeding 60% savings.
• Response Latency: Average decrease of 19.0%, with intersection performance queries achieving a 23.7% improvement.
• Multi-Query Processing: Up to 37.6% efficiency gain for complex, combined queries.
• Operational Cost: 61.5% reduction in monthly costs for a typical traffic management center workload.
• Conversational Efficiency: For open-ended tasks, conversational rounds required dropped from 3.4 to 1.1 (67.6% improvement).

Notably, the system maintains or improves output quality and correctness across all evaluated scenarios. The only observed regressions were in simple visualization tasks, where graph construction overhead outweighed parallelization benefits.

Practical Implications

GraphTrafficGPT's architecture directly addresses the operational needs of modern traffic management:

• Scalability: The system's efficiency gains scale with task complexity, making it suitable for large, heterogeneous urban environments.
• Real-Time Responsiveness: Reduced latency and multi-query support enable timely interventions in dynamic traffic conditions.
• Cost-Effectiveness: Lower token usage and operational costs facilitate broader adoption, especially in resource-constrained municipalities.
• Integration Potential: The modular agent design and compatibility with existing TFMs support integration with urban management platforms and mobile applications.

Theoretical and Future Directions

The paper's results reinforce the superiority of graph-based over chain-based architectures for complex, interdependent reasoning tasks in LLM systems. The explicit modeling of task dependencies and parallel execution pathways aligns with findings in related domains, suggesting broad applicability beyond traffic management.

Future research directions outlined include:

• Dynamic Graph Construction: Adaptive graph generation based on query complexity to optimize performance.
• Enhanced Parallelization: Further exploitation of parallelism within and across agent workflows.
• Direct Agent Communication: Reducing mediation overhead by enabling agent-to-agent task forwarding.
• Dynamic Tool and Agent Creation: Autonomous expansion of system capabilities in response to emerging requirements.
• Cross-Modal and Multi-Platform Integration: Incorporation of sensor, visual, and additional simulation data sources.
• Adaptive Resource Allocation: Intelligent distribution of computational resources under varying system loads.
• Real-World Deployment Studies: Empirical validation in operational traffic management environments.

Conclusion

GraphTrafficGPT represents a significant advancement in the design of LLM-driven traffic management systems. By leveraging a graph-based, multi-agent architecture, it achieves substantial improvements in efficiency, scalability, and cost-effectiveness, while maintaining compatibility with established traffic modeling tools. The system's design principles and empirical results have direct implications for the deployment of AI-powered decision support in complex, real-world domains, and provide a foundation for future research in scalable, context-aware LLM applications.