AI-Powered Traffic Signal Control Systems for Enhanced Traffic Flow and Reduced Waiting Time

Abstract

Traffic congestion is a persistent challenge in urban areas, resulting in increased travel times, fuel consumption, and environmental pollution. Traditional traffic signal control systems often rely on fixed timing plans that fail to adapt to real-time traffic conditions, leading to inefficiency and delays. Artificial intelligence (AI) has emerged as a promising tool for improving traffic signal control, providing the ability to dynamically adjust signal timings based on real-time traffic data and patterns. This paper presents a novel approach to designing AI-powered traffic signal control systems that aim to improve traffic flow and reduce waiting time. The proposed system uses a combination of machine learning algorithms and real-time traffic data to dynamically adjust signal timings. Machine learning models are trained on historical traffic data to identify patterns and relationships between traffic variables. Real-time traffic data is collected from sensors embedded in road infrastructure, such as cameras, detectors, and vehicle-to-infrastructure (V2I) communication systems. The system continuously analyzes historical and real-time data to predict future traffic conditions and optimize signal timings accordingly. The proposed AI-powered control system offers several advantages over traditional systems, including: Real-time adaptability, where the system dynamically adjusts signal timings based on real-time traffic conditions to achieve efficient flow; Reducing waiting time at intersections, which shortens travel times and improves fuel efficiency by optimizing signal timings; In addition to improving traffic flow, the system can effectively balance traffic flow across multiple intersections, reducing congestion and increasing overall network throughput; Predictive traffic management, where real-time data analysis helps predict future conditions and proactively adjust signal timings, to mitigate potential congestion before it occurs. The implementation of AI-powered traffic signal control systems has the potential to revolutionize urban traffic management, leading to significant improvements in traffic flow, reducing congestion, and enhancing safety for all road users.