AI-Powered Traffic Management: Improving Congestion Detection and Signal Regulation

This study presents an innovative approach to managing traffic congestion through an intelligent system that combines advanced congestion detection with adaptive signal control. The system leverages artificial intelligence to process various data sources, including real-time traffic flow, road conditions, and historical data, enabling it to detect congestion patterns with high accuracy. Machine learning algorithms are employed to analyze these patterns, allowing the system to automatically assess congestion levels and optimize signal timing dynamically based on factors such as traffic volume, queue length, and delay. The effectiveness of this approach has been validated through simulation studies, where the system demonstrated impressive performance metrics: an accuracy of 94.89%, a precision of 97.40%, a recall of 96.38%, and an F1 score of 96.76%. These results highlight the system's capability to effectively manage traffic conditions by accurately detecting congestion and minimizing false positives. By integrating these advanced techniques, the proposed solution significantly enhances traffic flow, reduces travel time, and improves the overall driving experience.