A Novel Approach to Mitigating Traffic Issues through Advanced Congest Detection and Adaptive Signal Control Using AI

This paper presents a novel approach to mitigating traffic issues through the development of an AI- driven intelligent traffic congestion identificationand signal switching system. The proposed system aims to improve traffic flow and reduce congestion by employing advanced congestion detection techniques and adaptive signal control mechanisms. The system utilizes state-of-the-art AI algorithms, such as machine learning and deep learning, to accurately identify congested areas in real-time. By analyzing various traffic parameters, includingtraffic flow, speed, and density, the system can effectively detect and predict congestion patterns. Once a congested area is identified, the system employs adaptive signal control strategies to optimize traffic signal timings and prioritize the flow of traffic in congested zones. This is achieved by dynamically adjusting signal timings based on the current traffic conditions, such as the number of vehicles waiting at each intersection and the expected delay. The system also integrates historicaltraffic data to further enhance congestion predictionaccuracy and signal switching decisions. Experimental results demonstrate the effectiveness of the proposed system in reducing traffic congestion and improving overall traffic performance. The AI-driven intelligent traffic congestion identification and signal switching system presented in this paper offer a promising and innovative solution to address traffic issues.