Neural Networks for Intelligent Transportation Systems

Abstract

Intelligent Transportation Systems (ITS) are revolutionizing modern transportation infrastructure by using advanced technologies to improve the efficiency, safety, and sustainability of transportation networks. Neural networks, particularly deep learning models, are playing a crucial role in enhancing ITS by providing capabilities for traffic prediction, autonomous driving, route optimization, and incident detection. This article explores the applications of neural networks in ITS, focusing on their impact on traffic management, autonomous vehicles, and real-time decision-making systems. It also discusses the challenges, future trends, and ethical considerations associated with the integration of neural networks in transportation systems.