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 …

In order to effectively manage the use of existing infrastructures and prevent the emergency
caused by the large gathered crowd, the short-term passenger flow forecasting technology …

Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …

Congestion of traffic is a key problem faced in a majority of metro cities, especially in the
developing world. Traffic congestion comprises of queues, reduced speeds, and increased …

This study aimed to analyze traffic congestion in urban road networks. The speed
performance index was adopted to evaluate the existing road network conditions of …

Delft University of Technology oad traffic is the visible result of the complex interplay
between traffic demand (the amount and mix of vehicles arriving at a particular place and …

Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy
decisions in advanced traffic control and guidance systems. Recently, deep learning …

Accurate short-term prediction of traffic conditions on freeways and major arterials has
recently become increasingly important because of its vital role in the basic traffic …

Traffic state estimation (TSE) reconstructs the traffic variables (eg, density or average
velocity) on road segments using partially observed data, which is important for traffic …

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 …