Macroscopic traffic flow models are of paramount importance to traffic surveillance and
control. Before their employments in applications, the models need to be calibrated and …

The challenge of traffic state estimation (TSE) lies in the sparsity of observed traffic data and
the sensor noise present in the data. This paper presents a new approach–physics informed …

Road link speed is often employed as an essential measure of traffic state in the operation of
an urban traffic network. Not only real-time traffic demand but also signal timings and other …

Observability and controllability are two critical requirements for a partially observable
transportation system. This paper proposes a stepwise signal optimization framework with …

We present a macroscopic model-based approach for the estimation of the total density and
flow of vehicles, for the case of “mixed” traffic, ie, traffic comprising both ordinary and …

In recent years, rapid advances in information technology have led to various data collection
systems which are enriching the sources of empirical data for use in transport systems …

Calibration and validation techniques are crucial in assessing the descriptive and predictive
power of car-following models and their suitability for analyzing traffic flow. Using real and …

Near future travel-time information is one of the most critical factors that travellers consider
before making trip decisions. In efforts to provide more reliable future travel-time estimations …

For its robust predictive power (compared to pure physics-based models) and sample-
efficient training (compared to pure deep learning models), physics-informed deep learning …

The development of real-time road condition systems will better monitor road network
operation status. However, the weak point of all these systems is their need for …