Traffic Congestion Prediction Using Machine Learning Algorithm

Traffic congestion poses a significant challenge in urban environments, leading to delays, increased fuel consumption, and heightened environmental pollution. Addressing this issue, our study presents an innovative dynamic traffic signal control system designed to adapt to real-time traffic conditions. Traditional traffic management systems typically rely on fixed schedules, which are inefficient when dealing with fluctuating traffic volumes. In contrast, our proposed system leverages advanced technologies to optimize traffic flow dynamically. At the core of the system are convolutional neural networks, which process video footage from CCTV cameras to accurately detect and count vehicles. This real-time data enables the system to dynamically adjust traffic signal timings, prioritizing lanes with heavier congestion and minimizing overall delays. By effectively responding to changing traffic patterns, the system reduces wait times and alleviates congestion, as validated through extensive simulations and experimental studies. The proposed approach is both cost-effective and scalable, making it particularly suitable for densely populated regions, such as urban centers in India. Additionally, the system capitalizes on existing infrastructure, providing a practical and efficient solution to urban traffic challenges. Future developments aim to integrate features like emergency vehicle detection and weather condition monitoring, further enhancing the system's responsiveness and optimizing urban traffic management.