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Distributed model predictive control for railway traffic management
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DOI：10.1016/j.trc.2016.05.006.png)
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Abstract
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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. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Railway traffic management
Dispatching support
Mixed integer optimization
Distributed model predictive control
Disturbance management
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