摘要：

Urban traffic congestion is a growing problem in cities across the globe, contributing to long delays, higher fuel consumption, environmental degradation, and economic losses. Conventional management systems often depend on static data rule-based approaches, which fall short dealing with complexity variability of modern traffic. This paper introduces an AI-based approach that utilizes machine learning models provide real-time forecasts. By integrating historical data, live sensor inputs, techniques, this system aims enhance flow, alleviate congestion, improve travel efficiency. The model compared against existing systems, demonstrating improved accuracy, flexibility, scalability. Results indicate offers significant advantages managing urban traffic, surpassing traditional methods. study further elaborates how AI-powered can cut times by ensuring efficiency safety, therefore playing part sustainability through emission reduction, opportunities challenges it brings table implementation AI systems.

DOI：10.46632/jbab/3/3/9