A Parameter Identification Scheme for Second-Order Highway Traffic Model Based on Differential Algebraic Methodology

Li, Nan; Zhao, Guangzhou

doi:10.1007/978-3-642-37105-9_10

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method was also used in and for parametric estimation with real time data. The same work as , where real data was used for all the parameter estimations, was reproduced by using empirical data without estimation of all the parameters. It is important to underline that the method is applied to obtain an estimate of the derivative from any signal, thus avoiding reliance on model of the system at least in the estimation of states .…”

Section: Introductionmentioning

confidence: 99%

Nonlinear State Estimation and Control for Freeway On‐Ramp Metering

Abouaïssa

Majid

Jolly

2016

Asian Journal of Control

View full text Add to dashboard Cite

The work presented in this paper deals with freeway ramp metering using a differential flatness concept. Such an approach is deployed in the cases when the traffic data provided by loop detectors (or any measurements device), are partially unknown or missed, and/or the downstream measurement station, located at the vicinity of the controlled ramp, is faulty. The proposed solution rests on the estimation of the main variables using the “revised” method of numerical differentiation, i.e., estimation of the derivative of noisy time signals. Such an algebraic approach, which does not need any integration of differential equations, turns out to be quite robust with respect to perturbations inputs and measurements noises. Convincing numerical simulations are provided using a modified second‐order continuum macroscopic model. Furthermore, a comparative study using the Extended Kalman Filter (EKF), which is the most used in the area of traffic state estimators, is proposed in order to demonstrate the effectiveness of the strategy.

show abstract

Section: Introductionmentioning

confidence: 99%