Drones Optimization for Public Transportation Safety: Enhancing Surveillance and Efficiency in Smart Cities

In the context of art cities, the adoption of multi-UAV systems has become a key focus in enhancing traffic management, particularly to fortify public safety. This study addresses the challenge of optimizing traffic management through the application of swarm-based Unmanned Aerial Vehicles (UAVs). The research strategically aims to minimize the number of deployed drones for monitoring extensive road networks, fostering cost-efficiency within smart city contexts. Our investigation introduces a mathematical model, the swarm drone set covering problem, to optimize coverage. Through a detailed computational experiment, we showcase the effectiveness of the algorithm in minimizing deployment while maintaining surveillance efficiency. Notably, our results reveal a significant correlation: as the radius of coverage for individual UAVs increases, the required number of UAVs decreases, underscoring the impact of coverage radius on resource optimization. The findings of this study contribute to the advancement of safety, security, and overall transportation network management in smart cities.