AI-Driven Traffic Management System with Graph Convolutional Neural Network, 5G- Enabled V2X Communication, and IoT for Emergency Vehicle Prioritization

Efficient management of traffic during emergencies is a critical challenge that directly impacts the ability to save lives and minimize delays in response. This research introduces a comprehensive and integrated system that combines the power of Artificial Intelligence (AI), 5G-enabled Vehicle-to-Everything (V2X) communication, Internet of Things (IoT)-based traffic signal controllers, and advanced Graph Convolutional Neural Networks (GCNs). Together, these technologies form a robust framework for prioritizing emergency vehicles, reducing traffic congestion, and optimizing response times in real-world scenarios. At the core of this system lies the integration of Raspberry Pi-based prototypes, which provide a scalable, cost-effective, and energy-efficient foundation for real-time traffic management. These devices enable decentralized processing, allowing the system to adapt to varying traffic conditions dynamically. The GCN models play a pivotal role in this architecture, achieving an impressive accuracy of 97% in node prioritization tasks. By analyzing both historical traffic patterns and real-time data streams, these models identify high-priority nodes and forecast congestion trends with exceptional precision