This paper examines the use of deep recurrent neural networks to classify traffic patterns in
smart cities. We propose a novel approach to traffic pattern classification based on deep …

This research explores the increasing applications of deep learning in traffic management
systems, with a focus on traffic prediction, object detection and recognition, autonomous …

The availability of traffic data and computational advances now make it possible to build
data-driven models that capture the evolution of the state of traffic along modeled stretches …

Smart city networks involve many applications that impose specific Quality of Service (QoS)
requirements, thus representing a challenging scenario for network management. Solutions …

This survey paper describes the significant literature survey of Sustainable Smart Cities
(SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection …

This study proposes a framework to classify traffic flow states. The framework is capable of
processing massive, high-density, and noise-contaminated data sets generated from …

Smart cities have revolutionized urban living by incorporating sophisticated technologies to
optimize various aspects of urban infrastructure, such as transportation systems. Effective …

In the development of smart cities, the intelligent transportation system (ITS) plays a major
role. The dynamic and chaotic nature of the traffic information makes the accurate …

The right choice of features to be extracted from individual or aggregated observations is an
extremely critical factor for the success of modern network traffic classification approaches …

Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It
aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion …