Assessment of the effectiveness of adaptive traffic signal control systems in reducing pedestrian-vehicle conflicts at high-risk crossings

Adaptive Traffic Signal Control (ATSC) systems represent a critical advancement in urban traffic management, offering significant potential for reducing pedestrian-vehicle conflicts at high-risk crossings. This review paper examines the effectiveness of ATSC systems in enhancing pedestrian safety through real-time signal optimization and intelligent traffic management. We analyze recent developments in adaptive signal technologies, including machine learning-based systems, connected vehicle integration, and multi-modal optimization approaches that prioritize pedestrian safety alongside traffic efficiency. The paper explores various ATSC architectures, from basic actuated systems to sophisticated deep reinforcement learning models, and their performance in reducing conflict points between pedestrians and vehicles. Recent field studies demonstrate that advanced ATSC systems can reduce pedestrian-vehicle conflicts by up to 40% while simultaneously improving overall traffic flow efficiency. However, challenges persist in balancing competing demands between vehicular throughput and pedestrian safety, particularly in high-density urban environments. This review synthesizes current research findings, identifies implementation barriers, and highlights the critical role of real-time pedestrian detection technologies in enabling safer adaptive signal control. Our analysis reveals that while ATSC systems show considerable promise for improving pedestrian safety, their effectiveness varies significantly based on intersection geometry, traffic patterns, and system sophistication. The integration of emerging technologies such as computer vision, artificial intelligence, and vehicle-to-infrastructure communication presents opportunities for next-generation ATSC systems that can more effectively balance safety and efficiency objectives.