Traffic state prediction is a key problem with considerable implications in modern traffic
management. Traffic flow theory has provided significant resources, including models based …

The stochastic cell transmission model (SCTM) was originally developed for stochastic
dynamic traffic state modeling under several assumptions, eg, the independent/uncorrelated …

Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …

Freeway traffic state estimation is a vital component of traffic management and information
systems. Macroscopic-model-based traffic state estimation methods are widely used in this …

The paper proposes a model-based framework for estimating traffic states from Eulerian
(loop) and/or Lagrangian (probe) data. Lagrangian-Space formulation of the LWR model …

Predicting traffic states has gained more attention because of its practical significance.
However, the existing literature lacks a critical review regarding how to address the …

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 …

Traffic prediction is critical for the success of intelligent transportation systems (ITS).
However, most spatio-temporal models suffer from high mathematical complexity and low …

Modern traffic control and management systems in urban networks require real-time
estimation of the traffic states. In this paper, a novel approach for modeling traffic flow in …

In real-time traffic management and intelligent transportation systems (ITS) applications, an
accurate picture of the prevailing traffic state in terms of speeds and densities is critical, for …