AI for Traffic Prediction and Management

Abstract

Urban traffic congestion is a growing problem in cities across the globe, contributing to long delays, higher fuel consumption, environmental degradation, and economic losses. Conventional traffic management systems often depend on static data and rule-based approaches, which fall short in dealing with the complexity and variability of modern traffic. This paper introduces an AI-based traffic management approach that utilizes machine learning models to provide real-time traffic forecasts. By integrating historical data, live sensor inputs, and machine learning techniques, this system aims to enhance traffic flow, alleviate congestion, and improve travel efficiency. The model is compared against existing systems, demonstrating improved accuracy, flexibility, and scalability. Results indicate that the AI-based system offers significant advantages in managing urban traffic, surpassing traditional methods. The study further elaborates on how AI-powered traffic management can cut travel times by ensuring efficiency and safety, therefore playing a part in environmental sustainability through emission reduction, and the opportunities and challenges it brings to the table in the implementation of AI in traffic systems.