AI-Based Intelligent Traffic Signal Management System: A Review - Divya Narchal; Amrik Singh; Dr. Manpreet Kaur (International Journal of Electronics Automation, 2025)
Traffic congestion is a growing problem in urban areas worldwide, leading to economic losses, increased pollution, and commuter frustration. Traditional traffic management systems rely on fixed timing cycles and lack adaptability to real-time traffic conditions. Intelligent traffic light control systems based on artificial intelligence (AI) have become a viable substitute for traditional techniques. These systems are able to evaluate large volumes of traffic data in real time, identify patterns, and dynamically adjust signal timings by utilizing sophisticated computational techniques including machine learning, deep learning, reinforcement learning, and computer vision. In contrast to conventional systems, AI-driven methods have the capacity to self-learn, continuously enhance decision-making, and adjust to changes in road usage, traffic density, and outside variables like bad weather or accidents. With an emphasis on how sensor technology and optimization algorithms enable real-time decision-making, this paper investigates the incorporation of AI approaches into traffic light control. Additionally, it looks into computer vision for vehicle recognition and traffic density prediction, as well as reinforcement learning for adaptive traffic flow management. In addition, practical applications of AI-powered traffic control systems are examined to demonstrate how well they work to ease traffic, cut down on travel time, and enhance urban mobility in general. The potential of AI-driven traffic management systems is further supported by real-world applications. Major city pilot projects have shown reduced carbon emissions, increased fuel economy, and shorter commute times. For instance, AI-powered adaptive traffic signal management systems have successfully decreased junction wait times and increased peak-hour traffic flow efficiency. By facilitating collaborative and anticipatory traffic control, integration with cutting-edge technology like connected and autonomous vehicles (CAVs) further enhances the potential of AI-based solutions. Urban traffic control is undergoing a paradigm shift with AI-based intelligent traffic signal management technologies. These technologies might greatly reduce traffic, cut emissions, and enhance the quality of life in cities across the globe by substituting flexible, data-driven decision-making processes with strict, pre-programmed cycles. The combination of AI, IoT, and smart infrastructure offers a promising route toward sustainable, effective, and future-ready transportation networks, even while issues like cost, data privacy, and scalability still exist.
Keywords: AI-based traffic signal control; Adaptive traffic signals; Real-time traffic optimization; Smart cities; Traffic congestion; Urban traffic management