Reference
T. Bellemans, B. De Schutter, G. Wets, and B. De Moor, "Model predictive
control for ramp metering combined with extended Kalman filter-based traffic
state estimation,"
Proceedings of the 2006 IEEE Intelligent
Transportation Systems Conference (ITSC 2006), Toronto, Canada, pp.
406-411, Sept. 2006.
Abstract
Ramp metering is a dynamic traffic control measure that has proven to be very
effective. There are several methods to determine appropriate ramp metering
signals for a given traffic situation. In this paper, a framework consisting of
model predictive control (MPC) for ramp metering, combined with extended Kalman
filter-based (EKF) traffic state estimation is presented. Based on traffic
measurements at a limited number of locations, the EKF is able to provide the
MPC ramp metering controller with estimations of the traffic states in the
motorway segments of the motorway stretch under control. By using the same
traffic flow model in the EKF as in the MPC prediction model, some important
model parameters of the MPC prediction model can be estimated and be fed
directly to the MPC controller. This functionality enables the MPC prediction
model to track changes in the traffic system (e.g. due to weather conditions,
incidents, etc.). The presented EKF-MPC controller for ramp metering is
simulated for a case study on the E17 motorway Ghent-Antwerp in Belgium.
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BibTeX
@inproceedings{BelDeS:06-021,
author = {Bellemans, Tom and De Schutter, Bart and Wets, Geert and De
Moor, Bart},
title = {Model Predictive Control for Ramp Metering Combined with
Extended {Kalman} Filter-Based Traffic State Estimation},
booktitle = {Proceedings of the 2006 IEEE Intelligent Transportation Systems
Conference (ITSC 2006)},
address = {Toronto, Canada},
pages = {406--411},
month = sep,
year = {2006}
}