Intelligent Traffic Signal Control And Connected Vehicle Integration For Urban Intersections: A Comprehensive Review

Abstract

Urban intersections are critical nodes in city traffic networks, where congestion, delays, and safety concerns are most pronounced. The rapid growth of vehicle ownership and increasing mobility demands place significant pressure on existing infrastructure, necessitating innovative traffic management strategies. This study presents a comprehensive analysis of intersection performance, integrating data-driven methodologies, real-time simulation models, and advanced optimization techniques. Connected vehicle technologies, adaptive signal control systems, and vehicle-to-infrastructure (V2I) communication are evaluated for their potential to improve traffic flow, reduce congestion, minimize travel times, and lower emissions. The research employs computational intelligence methods, including reinforcement learning, fuzzy logic, genetic algorithms, and multi-agent systems, to model dynamic traffic conditions and optimize signal operations. By analyzing various intersection configurations and traffic scenarios, the study demonstrates how intelligent systems can enhance individual junction performance while facilitating coordinated optimization across urban networks. The findings emphasize the importance of scalable, hybrid, and adaptive solutions to bridge the transition between conventional traffic management practices and a fully autonomous vehicle environment. This work provides actionable insights for urban planners and traffic engineers seeking to improve efficiency, safety, and sustainability in modern urban transportation systems.