Neural Networks for Real-Time Traffic Flow Prediction and Management

Abstract

Real-time traffic flow prediction and management are essential for improving urban mobility and reducing congestion. Neural networks, particularly deep learning models, have shown great promise in predicting traffic patterns and optimizing traffic flow. This article explores the use of neural networks in real-time traffic flow prediction, focusing on their ability to analyze large-scale traffic data and make accurate predictions in dynamic traffic environments. We examine various neural network models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), and their applications in real-time traffic management. Additionally, the article discusses the challenges, ethical considerations, and future directions of using neural networks for traffic flow prediction and management..