Smart Traffic Signal Optimization Using Real-Time Data: A Review

Rapid urbanization and increased vehicular traffic demanded the creation of intelligent traffic management systems. Smart traffic signal optimization employing real-time data has emerged as a critical approach for improving traffic flow, reducing congestion, and increasing overall urban mobility. This research paper digs into improvements in traffic signal control systems, focusing on the integration of real-time data and artificial intelligence (AI) approaches. We investigate a variety of methodologies, including reinforcement learning, fuzzy logic, and connected car technologies, highlighting their strengths and limits. The report also analyses the problems and potential future approaches for deploying these intelligent systems.