A model was formulated for estimating dynamic origin-destination (OD) travel time and flow
on a long freeway with a neural Kalman filter originally developed by the authors. The model …

A dynamic model based on the error back-propagation learning principle in neural network
theory is proposed for estimating origin-destination flows from the road entering and exiting …

Two distinct ways of using Kalman filters to address the problem of short-term urban arterial
travel time prediction have been presented in this paper. One is to train a neural network by …

This paper presents a novel approach for freeway traffic state estimation. Although there is
currently a wide variety of estimation methods, new designs and technologies can still be …

This study develops a dynamic short-term corridor travel time prediction model using the
traffic flow data collected by single loop detectors. This method involves a multi-step-ahead …

In the present research, a nonlinear Kalman filtering approach, ie, extended Kalman filter
(EKF) was proposed to solve dynamic OD flows and travel times on a freeway segment. The …

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 …

Recent researches have shown the potential benefits of using Lagrangian coordinates in
modeling mobile sensor data such as GPS, Bluetooth, Wi-Fi, and cellphone probe data …

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 …

A neural network model was integrated into the Cremer model for estimating traffic states on
a freeway. In the Cremer model, which is a macroscopic traffic flow model combined with the …