AI for Real-Time Traffic Management in Communication Networks

Real-time traffic management has grown into a critical dilemma due to rising communication network requirements. The promising solutions helps manage traffic flow while simultaneously lowering congestion and increasing the efficiency of networks. The paper evaluates artificial intelligence techniques with machine learning and deep learning and reinforcement learning as tools for predicting traffic and managing congestion while allocating resources. The paper examines published studies, analyzes AI model approaches, and assesses the performance of AI models to has established that artificial intelligence constitutes a viable solution to build adaptable automated network management systems for traffic control.