Real-Time Traffic Optimization for Urban Traffic Congestion

Traffic gridlock is a significant issue in urban centers with growing populations. The increased vehicle congestion has severe consequences, including elevated air and noise pollution levels, higher fuel consumption, and increased stress among commuters. These conditions disrupt daily routines and adversely affect economic productivity and public health. Many cities rely on outdated traffic management systems that operate on fixed schedules and lack real-time adaptability, which worsens congestion. As vehicle numbers surge, replacing these conventional systems with more advanced solutions becomes crucial. The proposed approach emphasizes the integration of machine learning algorithms for real-time vehicle detection and traffic analysis. By comparing various machine learning models, this research aims to identify the most effective algorithm for managing urban traffic, enabling a responsive system that adjusts to live traffic conditions. This method offers a promising solution to alleviate gridlock, address community needs, and reduce the environmental and social costs associated with traffic congestion