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Rapid population growth and the increasing demand for transportation have led to a pervasive issue of traffic congestion in various cities, with Houston, Texas, USA, being notably affected. The consequences of congestion, whether direct or indirect, impact our daily lives significantly. These consequences encompass loss of productive hours, higher accident rates, road rage incidents, environmental pollution, and unpredictability in travel times. The continued rise in traffic necessitates innovative solutions like Artificial Intelligence (AI) for improved signal control and efficient traffic management. Among the fundamental components governing traffic flow, traffic controllers play a crucial role. Similar to advancements in electronics, traffic lights must evolve to address the evolving challenges of congestion and population growth. One proposed solution involves real-time data collection through cameras positioned at traffic intersections. AI and image processing technologies can be leveraged to analyze these images, enabling the determination of traffic density and facilitating realtime traffic direction based on vehicle volume. The key innovation lies in transforming individual traffic lights within a specific zone or area into a synchronized system managed by a central control unit. This approach results in a unified and efficient traffic management system. By implementing such a system, cities can better respond to the growing demands of their transportation networks, alleviate congestion, and enhance the overall commuting experience for residents. This integrated approach represents a strategic and technology-driven solution that harnesses the power of AI and realtime data to optimize traffic management, making it responsive to the challenges presented by rapid urbanization and increased vehicular traffic. In doing so, it aims to improve the quality of life for residents by reducing the negative impacts of congestion and enhancing the efficiency of urban transportation. 

KEYWORDS: Artificial Intelligence, Traffic Light, Traffic management and Image processing. 

Olatunji Ahmed. A. And Femi Ekanoye, Ph.D
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