Generating optimal schedules for an underground railway line

Minciardi, R.; Paolucci, Massimo; Presenti, R.

doi:10.1109/cdc.1995.479246

Cited by 13 publications

(8 citation statements)

References 4 publications

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“…In Braker (1991, 1993), de Vries et al (1998, Heidergott and Vries (2001), de Waal et al (1997), and Minciardi et al (1995 it has been shown that a model of a railway network, with a fixed routing schedule and fixed connections, can be described as a max-plus-linear model. In max-plus algebra such a model is 'linear'.…”

Section: Introductionmentioning

confidence: 98%

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

Kersbergen

Rudan

Boom

et al. 2014

Discrete Event Dyn Syst

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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.Keywords Max-plus algebra · Railway networks · Rescheduling · Model reduction · MILP B. Kersbergen ( ) · T. van den Boom · B. De Schutter

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Section: Introductionmentioning

confidence: 98%

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

Kersbergen

Rudan

Boom

et al. 2014

Discrete Event Dyn Syst

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“…Closed-loop metro systems, e.g. Assis and Milani (2004); Cury et al (1980); Minciardi et al (1995). 2.…”

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confidence: 99%

Automated timetable design for demand-oriented service on suburban railways

Albrecht¹

2008

Public Transp

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Economic and attractive operation of suburban railways can only be realised by flexibilisation of headways, adaptation of the network and capacity of the different lines. Under those circumstances, the computation of optimal operation programmes is very complex. This contribution presents a two-level approach (computation of transport offer, timetable design) and shows the results obtained from fullyautomatic offer planning and timetabling for a suburban railway. MotivationDemand-oriented metro systems have proven their commercial success for more than 20 years in practice (Strobel and Schütte 2004). The reason of success for this kind of transport system is the so-called flexible operation: the headways between the small capacity trains are chosen in such a way (temporal flexibilisation) that the trains are equally high occupied. One example of this operating strategy is the fully automatic underground metro (AGT-Automated Guided Transit) of the city of Lille in northern France, which operates at headways between 1 min and 6 min (cf. Fig. 1).Suburban railway systems in medium-sized conurbations, on the contrary, are mainly operated with long trains at long constant headways most of the day. This paper examines how the flexible operation, i.e., using small vehicles at short intervals for the strict adaptation of transport offer to demand, can be introduced on suburban railway systems and how a timetable can be created in this case.The literature on demand-oriented timetabling of public transit systems mainly focusses on two points:

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“…Note that we do not consider re-routing or adapting the time schedule. Work in connection with the modeling and control of railway networks in a systems and control context can be found in [2,3,7,12]. We also refer the interested reader to [9,10,11,13] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Approach for Recovery from Delays in Railway Systems

Schutter

Boom

Hegyi

2002

Transportation Research Record: Journal of the Transportation R

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We extend the model predictive control (MPC) framework, which is a very popular controller design method in the process industry, to transfer coordination in railway systems. In fact, the proposed approach can also be used for other systems with both hard and soft synchronization constraints, such as logistic operations. The main aim of the control is to recover from delays in an optimal way by breaking connections (at a cost). In general, the MPC control design problem for railway systems leads to a nonlinear non-convex optimization problem. We show that the optimal MPC strategy can also be computed using an extended linear complementarity problem. Furthermore, we present an extension with an extra degree of freedom to recover from delays by letting some trains run faster than usual (again at a cost). The resulting extended MPC railway problem can also be solved using an extended linear complementarity problem.

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Generating optimal schedules for an underground railway line

Cited by 13 publications

References 4 publications

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

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

Automated timetable design for demand-oriented service on suburban railways

Model Predictive Control Approach for Recovery from Delays in Railway Systems

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