Deep Neural Networks for Predicting Traffic Patterns in Smart Cities

Abstract

Traffic congestion is one of the most pressing challenges in urban planning, especially in smart cities that rely on data-driven systems to optimize infrastructure. This article explores the use of deep neural networks (DNNs) for predicting traffic patterns in smart cities, which can significantly enhance traffic management, reduce congestion, and improve overall urban mobility. We discuss the architecture of deep neural networks, their applications in predicting traffic flow, and the integration of real-time data from sensors and IoT devices. The article also highlights the potential of DNNs to predict traffic anomalies, optimize traffic signals, and provide actionable insights for urban planners and policymakers.