The exponential growth of urban vehicular traffic has rendered traditional timer-based signal control systems inefficient, leading to increased congestion, fuel wastage, and carbon emissions. This paper proposes a novel Drone-Based Traffic Density Control System that leverages Unmanned Aerial Vehicles (UAVs) equipped with ESP32-CAM modules for real-time, aerial surveillance of road intersections. Unlike fixed infrastructure, the proposed system utilizes a rotating camera mechanism to provide 360-degree coverage, eliminating blind spots. The system employs Edge AI for vehicle detection and density estimation, transmitting telemetry data via ESP-NOW Protocol to a ground-based traffic controller. This paper presents the mathematical modeling of the traffic flow using Webster's optimization logic and the PID stability analysis of the drone flight controller. Experimental results demonstrate that the system successfully adapts signal timing based on real-time density, significantly reducing average waiting time at intersections. In addition,the system incorporates emergency vehicle detection from the camera feed and immediately grants priority green to the corresponding approach, pre-empting the normal phase sequence to reduce emergency response time.
Kiran et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: