Optimizing Traffic Efficiency and Emergency Response through Enhanced Dynamic Charging Lanes, Autonomous Platooning Systems, and 6G Connectivity

• Enables real-time communication among vehicles, infrastructure, and the energy grid, optimizing charging, platoon formation, and emergency vehicle prioritization to enhance traffic flow, energy efficiency, and safety using 6G connectivity • The research combines CNNs, RNNs, and GNNs to predict vehicle movements, optimize charging schedules, and form efficient platoons using real-time data, improving traffic management and energy efficiency. • This work uses RNNs to predict emergency vehicle arrival times, dynamically adjusting traffic and charging schedules to reduce delays and enhance emergency response Rapid urbanization and the rising adoption of electric vehicles (EVs) are placing increasing pressure on modern transportation systems, particularly in the areas of congestion management, energy coordination, and emergency response. These challenges motivate the development of intelligent transportation systems capable of leveraging emerging 6G connectivity and advanced deep learning techniques. In this work, we present an integrated framework that combines convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs) to support a unified traffic-management architecture. The system is designed to provide three sequential capabilities: CNN-based prediction of vehicle arrival characteristics, RNN-based scheduling for dynamic traffic and charging events, and GNN-based optimization of vehicle interactions such as platoon formation. These components operate within a 6G-assisted VANET simulation, enabling evaluation of communication-network performance under high-density urban conditions. The results primarily validate the network-level behavior of the proposed framework, demonstrating stable throughput and reduced end-to-end delay under dynamic traffic loads. Preliminary deep-learning performance indicators, including a 90% training accuracy on synthetic SUMO-generated data, confirm the feasibility of the integrated model but do not yet constitute full validation of the charging, platooning, or emergency-response capabilities. Accordingly, the study concludes by outlining the need for future work involving real-world datasets, expanded evaluation metrics, and comparative baselines to rigorously assess the full potential of the proposed multi-stage architecture.