AI & Machine Learning for Traffic Prediction and Management

Abstract

Rapid urbanization and growth of vehicles have created serious traffic congestion problems in cities across the world. Traditional traffic management methods are no longer sufficient to handle the dynamic and complex nature of modern transportation systems. Artificial Intelligence (AI) and Machine Learning (ML) techniques offer powerful tools for traffic prediction, congestion control, and real-time traffic management. These technologies can analyze large amounts of traffic data collected from sensors, GPS devices, cameras, and mobile applications to predict traffic flow and optimize transportation networks. This paper presents a detailed review of AI and ML applications in traffic prediction and management. Various algorithms such as Artificial Neural Networks, Support Vector Machines, Random Forest, Deep Learning models like LSTM and CNN, and Reinforcement Learning are discussed. The study also highlights data sources, system architecture, challenges, and future research directions. Tables are provided to compare different models and their performance. The paper aims to give a comprehensive understanding of how intelligent systems can improve urban mobility and reduce traffic related issues.

KEYWORDS: Traffic prediction, Machine learning, Artificial intelligence, Intelligent transportation systems, Deep learning, Traffic management.