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.