Reference
B. Kersbergen, T. van den Boom, and B. De Schutter, "Distributed model
predictive control for railway traffic management,"
Transportation Research Part C, vol. 68, pp. 462-489, July
2016.
Abstract
Every day small delays occur in almost all railway networks. Such small delays
are often called "disturbances" in literature. In order to deal with
disturbances dispatchers reschedule and reroute trains, or break connections.
We call this the railway management problem. In this paper we describe how the
railway management problem can be solved using centralized model predictive
control (MPC) and we propose several distributed model predictive control
(DMPC) methods to solve the railway management problem for entire (national)
railway networks. Furthermore, we propose an optimization method to determine a
good partitioning of the network in an arbitrary number of sub-networks that is
used for the DMPC methods. The DMPC methods are extensively tested in a case
study using a model of the Dutch railway network and the trains of the
Nederlandse Spoorwegen. From the case study it is clear that the DMPC methods
can solve the railway traffic management problem, with the same reduction in
delays, much faster than the centralized MPC method.
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BibTeX
@article{Kervan:16-013,
author = {Kersbergen, Bart and van den Boom, Ton and De Schutter, Bart},
title = {Distributed Model Predictive Control for Railway Traffic
Management},
journal = {Transportation Research Part C},
volume = {68},
pages = {462--489},
month = jul,
year = {2016}
}