Evaluation of Adaptive Traffic Signal Control using Smart Technologies

Computer Engineering

The increasing levels of urban traffic congestion, frequent delays, and growing concerns about road safety have made effective traffic management a pressing issue in modern cities. Conventional traffic systems, which typically operate with fixed signal timings and limited manual interventions, often prove inadequate in responding to dynamic traffic patterns and fluctuating road conditions. To address these challenges, Smart Traffic Management Systems (STMS) have emerged as promising alternatives, offering real-time, data-driven solutions for enhancing traffic flow and safety. STMS make use of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), the Internet of Things (IoT), and advanced sensor networks to collect and analyse traffic data. These systems are capable of adjusting traffic signals based on real-time conditions, predicting congestion, detecting violations, and even prioritizing emergency vehicle movement. From 2020 to 2024, significant advancements have been made in the field, including the use of Graph Neural Networks (GNNs), deep reinforcement learning algorithms, and adaptive traffic signal systems. Despite these achievements, smart traffic systems still face hurdles. Issues such as the high cost of infrastructure, concerns over data privacy and cybersecurity, and compatibility between various technologies must be overcome for large-scale implementation. This study offers a comprehensive review of recent progress in smart traffic technologies, highlights successful real-world applications, and discusses ongoing challenges and future opportunities for developing intelligent and efficient urban transportation networks.

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