Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …

Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …

We propose a kinematic wave-based Deep Convolutional Neural Network (Deep CNN) to
estimate high-resolution traffic speed fields from sparse probe vehicle trajectories. We …

Traffic state estimation (TSE) plays a significant role in traffic control and operations since it
can provide accurate and high-resolution traffic estimations for locations without traffic states …

Rapid urbanization and globalization have resulted in intolerable congestion and traffic,
necessitating the investigation of Intelligent Transportation Systems (ITS). ITS employs …

Due to higher energy efficiency and lower emissions, electric vehicles (EVs) have become
attractive transportation means in develo** cleaner mobility systems. Moreover, many …

Rising demand for ride-hailing services and e-commerce delivery intensifies competition for
urban curbside spaces, leading to uncoordinated travel behavior, increased traffic …

Recent studies have shown the successful implementation of classical model-based
approaches (eg macroscopic traffic flow modelling) and data-driven approaches (eg …

In this paper, we develop new methods to join machine learning techniques and
macroscopic differential models, aimed at estimate and forecast vehicular traffic. This is …

Quality data are vital to the planning and operation of traffic systems. High occupancy
vehicle (HOV) lanes, for instance, must comply with federal performance standards. If an …