Drone-Enabled Aerial Monitoring for Dynamic Traffic Control Using Multi-Modal Data Synthesis

Abstract

Dynamic traffic control is a critical challenge in urban environments, where conventional monitoring systems often fail to provide real-time, accurate, and adaptable solutions. Drone-enabled aerial monitoring has emerged as a promising technology to enhance traffic management by offering rapid deployment, high mobility, and an expansive field of view. This paper presents a novel framework for drone-enabled traffic control utilizing multi-modal data synthesis. By integrating real-time video feeds, LiDAR data, and environmental sensors, the proposed system provides a comprehensive analysis of traffic conditions, enabling dynamic traffic signal adjustment, congestion mitigation, and emergency response optimization. The framework incorporates advanced machine learning algorithms for real-time object detection, vehicle classification, and predictive traffic flow modeling. Furthermore, the system ensures efficient data fusion from multiple drones and ground-based sensors using edge computing to minimize latency. Simulation and experimental results demonstrate the efficacy of this approach, achieving an average reduction of 25% in traffic congestion and a significant improvement in emergency vehicle response times. This study underscores the potential of drone-enabled systems in transforming urban traffic management, paving the way for smarter and more sustainable cities.