Predictive Traffic Regulation Methodologies Using 5G-Enhanced Sensor Fusion Across Vehicle and Drone Platforms

Abstract

High-speed wireless connectivity advances the integration of vehicles and drones, enabling new forms of sensor fusion for congestion monitoring and real-time traffic regulation. Fifth-generation (5G) mobile networks offer low-latency data exchange and scalable bandwidth, allowing unmanned aerial vehicles (UAVs) to coordinate with ground vehicles for route optimization and congestion forecasting. Sensor arrays composed of LiDAR, radar, and camera systems generate substantial streams of environmental data. Predictive analytics, bolstered by machine learning and domain-specific modeling, leverages these data to identify emerging bottlenecks and propose adaptive rerouting or traffic signal adjustments. Edge computing resources located at strategic nodes process sensor readings with minimal delay, alleviating burdens on central data centers. Data-driven control algorithms issue commands to vehicles and drones that operate in coordinated swarms, mapping roadway capacity and orchestrating alternative routes. Drone-mounted sensors augment ground-based measurements by covering areas beyond the line of sight of traffic cameras. Security protocols, built upon encrypted channels and tamper-proof frameworks, reinforce trust in data integrity and command authenticity. This paper examines the technical architecture of 5G-enhanced sensor fusion, highlights the core predictive modeling approaches, and evaluates multi-platform integration techniques for improving traffic throughput. Emphasis is placed on system orchestration and data-driven coordination principles that drive proactive interventions across various road networks.