The volume and availability of data in the Intelligent Transportation System (ITS) result in the
need for data-driven approaches. Big Data algorithms are applied to further enhance the …

The growing size of cities and increasing population mobility have determined a rapid
increase in the number of vehicles on the roads, which has resulted in many challenges for …

Accurate short-time traffic flow prediction has gained gradually increasing importance for
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …

In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic
flow with big data. While existing DNN models can provide better performance than shallow …

This paper proposes a convolutional neural network (CNN)-based method that learns traffic
as images and predicts large-scale, network-wide traffic speed with a high accuracy …

We develop a deep learning model to predict traffic flows. The main contribution is
development of an architecture that combines a linear model that is fitted using ℓ 1 …

Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been
the focus of many studies in the last few decades. Existing traffic flow prediction models …

Accurate and timely traffic flow information is important for the successful deployment of
intelligent transportation systems. Over the last few years, traffic data have been exploding …

Traffic flow prediction is a fundamental problem in transportation modeling and
management. Many existing approaches fail to provide favorable results due to being: 1) …