Machine Learning Models for Intelligent Traffic Management

Abstract

Intelligent traffic management is essential for improving road safety, reducing congestion, and optimizing transportation systems in urban environments. Machine learning (ML) models have emerged as a powerful tool for enhancing traffic management systems by enabling real-time decision-making and predictive analysis. This article explores the role of various machine learning models, including supervised and unsupervised learning techniques, in traffic management. It discusses the application of algorithms such as regression analysis, support vector machines, decision trees, and deep learning in traffic flow prediction, congestion control, and accident prevention. The article also highlights the challenges and future directions in deploying machine learning-based solutions for intelligent traffic systems.