The generation of trajectory data has increased dramatically with the advent and
widespread use of GPS-enabled devices. This rich source of data provides invaluable …

Machine learning (ML) and deep learning (DL) have become very popular in the research
community for addressing complex issues in intelligent transportation. This has resulted in …

Time series forecasting using historical data is significantly important nowadays. Many fields
such as finance, industries, healthcare, and meteorology use it. Profit analysis using …

Big data has a substantial role nowadays, and its importance has significantly increased
over the last decade. Big data's biggest advantages are providing knowledge, supporting …

In this study, we combine graph optimization and prediction in a single pipeline to
investigate an innovative convolutional graph-based neural network for urban traffic flow …

This article presents the deployment and experimentation architecture of the Aveiro Tech
City Living Lab (ATCLL) in Aveiro, Portugal. This platform comprises a large number of …

To achieve greater sustainability of the traffic system, the trend of traffic accidents in road
traffic was analysed. Injuries from traffic accidents are among the leading factors in the …

This paper aims to propose a novel Road Traffic Noise Model (RTNM), capable of
dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and …

This study proposes a multilayered deep neural network (MLDNN) and a congestion index
(CI) based on traffic density factor to forecast traffic congestion directly. Data were collected …

Eco-routing distributes traffic in cities to improve mobility sustainability. The implementation
of eco-routing in real-life requires a diverse set of information, including different kinds of …