Abstract
Rapid urbanization and the increasing number of vehicles have significantly contributed to traffic congestion in modern cities. Traffic congestion results in increased travel time, fuel consumption, environmental pollution, and economic losses. Conventional traffic management systems primarily depend on static signaling mechanisms and manual monitoring approaches, which are inadequate for handling dynamic urban transportation conditions. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies have enabled the development of intelligent transportation systems capable of real-time monitoring and predictive analytics. This paper presents an AI-based traffic congestion prediction system designed for smart urban transportation environments. The proposed framework integrates traffic sensors, cameras, GPS-enabled devices, and machine learning algorithms to continuously monitor traffic flow and predict congestion levels before critical conditions occur. Historical traffic datasets and real-time sensor inputs are analyzed using predictive models to identify congestion patterns and optimize traffic management decisions. The proposed system enhances traffic efficiency by supporting proactive traffic signal control, route optimization, and congestion mitigation strategies. The study emphasizes scalability, prediction accuracy, and sustainable transportation management. The integration of intelligent analytics with smart city infrastructure contributes toward safer, more efficient, and environmentally sustainable urban transportation systems.