Smart Traffic Control and Prediction Model Empowered with 5G Technology, Artificial Intelligence and Machine Learning

This research paper addresses the escalating challenges of urban traffic congestion and diminished driving safety resulting from the burgeoning automobile population. The study formulates an innovative framework for an Enhanced Traffic Management Model (ETMM) leveraging the convergence of Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. Through in-depth interviews with a diverse panel of experts and comprehensive data analysis, the framework integrates real-time and historical data streams for accurate traffic prediction, congestion estimation, and dynamic signal optimization. The findings reveal that the proposed ETMM substantially improves traffic flow, minimizes travel time, and enhances urban mobility. Additionally, the research demonstrates the viability of AI-driven approaches for traffic management, underscored by successful model deployment and validation. The future scope lies in refining the framework with continuous data updates, incorporating user-centric insights, and expanding its applications to create safer, more efficient urban transportation systems.