AI for Real-Time Traffic Management in Communication Networks

Authors

  • Gauri V. Sonawane Research Scholar, School of Computer Sciences and Engineering, Sandip University, India
  • Purushottam R. Patil Professor, School of Computer Sciences and Engineering, Sandip University, India
  • Pawan R. Bhaladhare Professor, School of Computer Sciences and Engineering, Sandip University, India

Keywords:

Artificial Intelligence, Real-Time Traffic Management, Communication Networks, Machine Learning, Network Optimization, Congestion Control.

Abstract

Real-time traffic management has grown into a critical dilemma due to rising communication network requirements. The application of Artificial Intelligence technology through its 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 enhance communication network operational efficiency. Research has established that artificial intelligence constitutes a viable solution to build adaptable automated network management systems for traffic control.

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How to Cite

Gauri V. Sonawane, Purushottam R. Patil, and Pawan R. Bhaladhare. 2025. “AI for Real-Time Traffic Management in Communication Networks”. Metallurgical and Materials Engineering 31 (1):475-85. https://metall-mater-eng.com/index.php/home/article/view/1271.

Issue

Vol. 31 No. 1 (2025)

Section

Research

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Copyright (c) 2025 Gauri V. Sonawane, Purushottam R. Patil, Pawan R. Bhaladhare

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