Drone Swarm Intelligence for Adaptive Traffic Management in Megacities

This article proposes a drone swarm intelligence framework for adaptive traffic management in megacities, addressing dynamic congestion challenges through real-time monitoring and optimization. Autonomous drone swarms collect high-resolution aerial imagery, processed via deep learning-based vehicle detection (YOLOv۴) to identify congestion patterns. A semi-decentralized reinforcement learning model enables collaborative decision-making, allowing drones to dynamically reroute traffic, prioritize high-congestion zones, and optimize signal controls. The system is validated through simulations in synthetic and real-world environments (e.g., Shenzhen, China), demonstrating a ۲۸% reduction in average travel delay and a ۳۳% improvement in traffic flow efficiency. Key contributions include scalability to urban complexity, resilience to communication failures, and compatibility with existing infrastructure. Results confirm the framework’s efficacy in mitigating congestion, reducing emissions, and enhancing urban mobility resilience, offering a practical approach for sustainable megacity transportation systems.

Keywords:

Drone Swarm Intelligence , Adaptive Traffic Management , Megacities , Deep Learning , Reinforcement Learning