This research proposes a short-term highway traffic state prediction method based on a
structural state space model, with the intention to provide a robust approach for obtaining …

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

A general approach to the real-time estimation of the complete traffic state in freeway
stretches is developed based on the extended Kalman filter. First, a general stochastic …

Traffic management and traffic information are essential in urban areas and require reliable
knowledge about the current and future traffic state. Parametric and nonparametric traffic …

Predictions of fundamental traffic variables in the short-term or near-term future are vital for
any successful dynamic traffic management application. Univariate short-term traffic flow …

In this paper, a Markov-based time series model (MTSM) framework is developed to predict
traffic network conditions by integrating archived and real-time data under various external …

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

An accurate and practical traffic-state estimation (TSE) method for signalized intersections
plays an important role in real-time operations to facilitate efficient traffic management. This …

Real-time traffic states and origin-destination (OD) flows are essential inputs for on-line
traffic control and management systems. With the final goal to unify the problem of real-time …

The objective of this paper is to present a new analytical tool that predicts highway
congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data …