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Towards railway traffic management using switching Max-plus-linear systems

机译:使用切换最大+线性系统进行铁路交通管理

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In this paper we present a railway traffic model and a model predictive controller for online railway traffic management of railway networks with a periodic timetable. The main aim of the controller is to recover from delays in an optimal way by changing the departure of trains, by breaking connections, by splitting joined trains, and - in the case of multiple tracks between two stations - by redistributing the trains over the tracks. The railway system is described by a switching max-plus-linear model. We assume that measurements of current running and dwell times and estimates of future running times and dwell times are continuously available so that they can be taken into account in the optimization of the system's control variables. The switching max-plus-linear model railway model is used to determine optimal dispatching actions, based on the prediction of the future arrival and departure times of the trains, by recasting the dispatching problem as a Mixed Integer Linear Programming (MILP) problem and solving it. Moreover, we use properties from max-plus algebra to rewrite and reduce the model such that the MILP problem can be solved in less time. We also apply the algorithm to a model of the Dutch railway network.
机译:在本文中,我们提出了具有定期时间表的铁路交通模型和用于铁路网络在线铁路交通管理的模型预测控制器。控制器的主要目的是通过更改火车的出发点,断开连接,拆分连接的火车,以及(如果在两个站之间有多个轨道的情况下)通过在轨道上重新分配火车来以最佳方式从延迟中恢复。 。铁路系统用最大线性加切换模型来描述。我们假设当前运行和驻留时间的测量以及未来运行时间和驻留时间的估计是连续可用的,因此可以在优化系统控制变量时将它们考虑在内。切换最大加线性模型铁路模型通过将调度问题重铸为混合整数线性规划(MILP)问题并求解,从而基于对火车未来到达和离开时间的预测,来确定最佳调度行动它。此外,我们使用max-plus代数的属性来重写和简化模型,从而可以在更短的时间内解决MILP问题。我们还将算法应用于荷兰铁路网络的模型。

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