Real-Time Traffic Prediction And Management Using Deep Learning

Abstract

Urban congestion remains a persistent challenge, negatively impacting the environment, economy, and daily lives of commuters. Traditional traffic management approaches rely on static schedules and conventional machine learning models, which struggle with real-time adaptability. This research proposes a novel approach leveraging deep learning techniques, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to enhance traffic prediction accuracy. Additionally, the integration of an AI-driven fourth traffic signal light aims to optimize flow dynamically. This paper explores the methodology, implementation challenges, and potential benefits of this approach for smart city development.