Rapid urbanization and the continuous increase in vehicle population have led to severe traffic congestion in metropolitan areas, demanding intelligent and adaptive traffic control solutions. Conventional traffic signal systems operate using fixed timing mechanisms that fail to respond to real-time traffic variations, resulting in inefficient traffic flow and increased delays. This paper presents the development of a smart traffic management system integrating Artificial Intelligence-based vehicle density analysis with hardware-based adaptive traffic signal control. The proposed system utilizes camera modules to capture live traffic images at road intersections, where computer vision algorithms process video frames to detect and count vehicles in real time. The estimated traffic density is transmitted through serial communication to an Arduino-based microcontroller unit that dynamically controls traffic signal timing. The hardware controller adjusts signal duration according to lane congestion levels, thereby optimizing vehicle movement and minimizing waiting time. Experimental implementation demonstrates effective coordination between AI-based traffic analysis and embedded signal control hardware, achieving improved traffic flow efficiency compared to conventional fixed-time systems. The proposed hybrid architecture provides a scalable and cost-effective solution suitable for intelligent transportation and smart city applications.
P.Bharathi et al. (2026) studied this question.