Recently, development in intelligent transportation systems (ITS) requires the input of
various kinds of data in real-time and from multiple sources, which imposes additional …

This paper addresses real-time joint traffic state and model parameter estimation on
freeways using data from fixed sensors and connected vehicles. It investigates how the …

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 flow prediction requires learning of nonlinear spatio-temporal dynamics which
becomes challenging due to its inherent nonlinearity and stochasticity. Addressing this …

Real-time and accurate traffic state forecasting of urban roads is of great significance to
improve traffic efficiency and optimize travel routes. However, future traffic state forecasting …

Real-time information about the dynamic variation of turning rates at intersections is
essential for achieving a more efficient traffic control and management system. Limitations in …

This paper presents a framework for signalized road network predictive optimization using
real-time routing information from connected vehicles (CVs). An important feature of the real …

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic
management and control. However, due to the limited deployment of traffic sensors …

In recent decades, the traffic on road increased in a huge number. It is very important to
manage the safety of the humans as well as to make an efficient flow of the traffic. To …

Traffic state estimation is a challenging task due to the collection of sparse and noisy
measurements from specific points of the traffic network. The emergence of Connected and …