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7th International Conference on Railway Operations Modelling and Analysis - Rail Lille, Lille, France, 04-/04/2017 - 07/04/2017; Maintenance activities are necessary to maintain the functionality of the railway infrastructure. Commonly, timetables are elaborated by considering the infrastructure unavailability periods caused by the performance of maintenance activities. However, sometimes unplanned maintenance activities have to be introduced at short notice, and the timetable must be rearranged to respect the new unavailabilities. Moreover, most of these activities require the presence of special maintenance trains which are typically not scheduled in the timetable and may have an impact on other train circulations. In this paper, we propose a mixed-integer linear programming formulation that rearranges a timetable to cope with the performance of maintenance activities. It includes the consideration of maintenance trains and other specific constraints, such as temporary speed restrictions. In this formulation, the rearrangement of the timetable is optimized based on a microscopic representation of both the infrastructure and the rolling stock. We assess two algorithms founded on this formulation on a real case study in the French railway network and we show their practical applicability.

7th International Conference on Railway Operations Modelling and Analysis - Rail Lille, Lille , France, 04-/04/2017 - 07/07/2017; In railway, the saturation problem consists in scheduling extra train paths within an existing timetable, intended to be a fixed reference. Inserting extra train paths reduces the global timetable stability, if compared to that of the not-saturated timetable. A compromise should therefore be sought, introducing constraints on the number and characteristics of the saturating paths in order to keep the stability of saturated timetables within a desired range. In this study, this task is performed by imposing that saturating paths are separated by a given buffer time. The study aims to provide some figures about the way in which buffer time affects the number of saturating paths and the stability of the resulting timetable. We saturate a real timetable (concerning a 70 km long French railway) using a mixed integer linear programming algorithm, imposing seven increasing values of buffer time. We perform the saturation considering two different time windows (during peak and off-peak hours) and two sets of saturating path types. The stability of the saturated timetables is evaluated through OpenTrack simulations with perturbed traffic conditions. The simulations are carried out with two perturbation scenarios. In this way the influence on stability of the parameters combination used in saturation is assessed. Results are presented and discussed, highlighting the role of the buffer time and of the type of saturating paths in providing stable saturated timetables.

7th International Conference on Railway Operations Modelling and Analysis - Rail Lille, Lille, France, 04-/04/2017 - 07/04/2017; In this paper we propose a reformulation of RECIFE-MILP aimed at boosting the algorithm performance. RECIFE-MILP is a mixed integer linear programming based heuristic for the real-time railway traffic management problem, that is, the problem of re-routing and rescheduling trains in case of perturbation in order to minimize the delay propagation. The reformulation which we propose exploits the topology of the railway infrastructure. Specifically, it capitalizes on the implicit relations between routing and scheduling decisions to reduce the number of binary variables of the formulation. In an experimental analysis based on realistic instances representing traffic in the French Pierrefitte-Gonesse junction, we show the performance improvement achievable through the reformulation.

Measuring capacity of railway infrastructures is an open problem even in its definition. In this paper, we propose RECIFE-SAT, a MILP-based algorithm to quantify capacity by solving the saturation problem. This problem consists in saturating an infrastructure by adding as many trains as possible to an existing (possibly empty) timetable. In an experimental analysis performed in collaboration with the French infrastructure manager, we show the promising performance of RECIFE-SAT. To the best of our knowledge, RECIFE-SAT is the first algorithm which is shown to be capable of saturating rather large railway networks considering a microscopic infrastructure representation. This representation allows RECIFE-SAT to really quantify the actual capacity of the infrastructure considered.

Ce livrable Phare du COP 12-16 synthétise les travaux effectués au cours de ces quatre dernières années par les laboratoires ESTAS et LEOST dans le domaine de la régulation de trafic ferroviaire. Ces travaux ont été marqué par l'implication dans deux projets majeurs ; Le projet Predit SIGIFret réalisé entre 2012 et 2015 ainsi que leprojet Européen FP7 ON-TIME effectué entre 2011 et 2015.

WCRR 2016, 11th World Congress of Railway Research, Milan, Italie, 29-/05/2016 - 02/06/2016; RECIFE-MILP is a well performing heuristic to tackle the real-time railway traffic management problem. This is the problem of routing and scheduling trains in case of perturbation, aiming at the minimization of delay propagation. In this paper, we show how RECIFE-MILP has been successfully applied to different case-studies, in collaboration with European railway undertakings and infrastructure managers. Indeed, despite the positive results achieved in these applications, several open questions remain on how to actually deploy a tool as RECIFE-MILP in the practice. In the final part of this work, we focus on some of these questions and we describe our experience with respect to them.

La volonté politique de développer le fret ferroviaire se heurte à plusieurs problèmes qui le confinent au déclin par son manque de compétitivité. L'initiative MaxiPerfo, lancée en 2010 à la suite de l'Engagement National pour Fret Ferroviaire, a pour objectif de réaliser un saut de performance au fret ferroviaire par le biais du déploiement d'innovations techniques. Les innovations couvrent plusieurs composants du système ferroviaire comme la modernisation du matériel roulant, permettre la circulation de trains longs sur le réseau et l'introduction d'outils d'aide à la décision qui améliorent l'intégration des circulations de trains fret avec celles des trains de voyageurs. Le projet SIGIFret s'inscrit dans ce contexte et vise à étudier l'apport de ces outils d'aide à la décision dans les phases de gestion opérationnelle du trafic comme dans les phases amont de planification. SIGIFret est un projet du programme de recherche PREDIT avec pour partenaires SNCF Réseau et l'IFSTTAR. Le projet est structuré en deux parties. Dans la première partie du projet, l'objectif était d'évaluer l'apport d'un outil d'aide à la gestion opérationnelle du trafic. Pour cela, la ligne Paris-Le Havre a été choisie comme cas d'étude en raison de la mixité des circulations qui la traversent et de la présence de deux noeuds ferroviaires critiques : les gares de Mantes-La-Jolie de Rouen-Rive-Droite. Toute l'étendue de la ligne contrôlée par les postes de régulation de ces gares a été modélisée dans l'outil de simulation OpenTrack. Cet outil est capable de reproduire a un niveau microscopique le comportement des circulations à partir de la description de la topologie de la voie, du système de signalisation et de la dynamique des trains. Avec cet outil, on est donc en mesure d'évaluer l'impact de différentes décisions de régulation. La modélisation couvre 7 kilomètres de ligne autour de la gare de Mantes-la-Jolie et 27 kilomètres de ligne autour de Rouen-Rive-Droite. Pour chaque zone de régulation, nous avons considéré plusieurs scénarios de perturbations sur lesquels nous avons comparé quatre stratégies de régulation selon plusieurs indicateurs de performances lies à la régularité et à la consommation d'énergie. Toutes les stratégies de régulation sont appliquées à chaque scenario puis les décisions sont simulées dans l'outil OpenTrack afin d'évaluer les différents indicateurs. Les résultats montrent clairement l'apport d'un outil d'optimisation pour une meilleure intégration des trains fret et des trains de passagers au niveau de la gestion opérationnelle des circulations. L'objectif de la seconde partie du projet était de traiter le problème de l'intégration des trains de fret aussi au niveau de la phase de planification. Dans ce but, une méthode de calcul de la capacité a été conçue, elle a pour principe d'intégrer le maximum de trains supplémentaires à une grille horaire existante. Parmi ces trains supplémentaires, une attention particulière est portée aux trains de fret. Comme dans la première partie du projet, les zones autour des gares de Mantes-la-Jolie et de Rouen-Rive-Droite ont été considérées comme cas d'application, mais cette fois-ci en y incluant toute la section de ligne qui relie ces deux gares. Un algorithme d'optimisation bi-objectif a été développé pour l'évaluation de la capacité. Les deux objectifs considérés sont d'une part la maximisation du nombre de trains supplémentaires et d'autre part le nombre de trains de fret supplémentaires. Les contraintes de l'infrastructure de niveau microscopique ont été prises en compte pour être en cohérence avec le modèle utilisé dans la première partie. La méthode développée a permis de saturer la grille horaire actuelle sur différents horizons temporels. Pour la période couvrant l'heure de pointe et le flanc de pointe, la méthode a réussi à intégrer 332 trains supplémentaires avec plus de 100 trains de fret (plus du double du nombre actuellement présent dans la grille horaire). Cette grille horaire très dense peut ainsi fournir un indicateur de la capacité résiduelle de l'infrastructure considérée. De la même façon, cette méthode peut être utilisée pour l'évaluation de l'analyse de la capacité de différentes options de modification d'infrastructures ou lors des phases d'adaptation du service annuel pour satisfaire a des demandes de sillons de dernières minutes.

We propose an optimal train dispatching module which computes in real-time the train routing and scheduling that minimize delay propagation. For solving this real-time railway traffic management problem, we exploit a fixed-speed mixed-integer linear programming (MILP) formulation (Pellegrini et al., 2014). To take into account the operational constraints, we impose here a maximum computation time to the MILP solver: the resulting algorithm is a heuristic which returns the optimal solution as soon as the search process is completed (the optimal solution is found and its optimality is proven) or returns the incumbent solution if the maximum computation time is reached (Pellegrini et al., 2013). The object of this work is to compare through simulation the solutions proposed by this formulation with the current dispatching rule. The scenarios tackled include both passenger and freight traffic at peack time. The experiments which we present have been produced in the context of the PREDIT project SIGIFret (Simulation d'une gestion innovante des circulations fret).

La gestion du trafic ferroviaire s’effectue par choix de routage et d'ordonnancement des trains qui minimisent la propagation de retards en cas de perturbation. Dans cet article, nous proposons des inégalités valides et des choix algorithmiques permettant d’améliorer les performances d'une formulation de programmation linéaire en nombres entiers pour la gestion du trafic ferroviaire. Dans l'analyse expérimentale, nous montrons que ces propositions améliorent effectivement les temps de calcul pour résoudre de manière exacte les instances représentatives du trafic dans une jonction française complexe: la gare Lille-Flandres.

Unexpected events often perturb railway traffic. The impact of these events may be very remarkable in terms of delay propagation. We analyze a mixed integer linear programming (MILP) formulation which aims at minimizing the delay propagation when traffic is perturbed. It does so by modifying train routing and scheduling at junctions. This formulation is able to solve to optimality many realistic instances in a computation time which is in line with real-time purposes. However, for the most difficult instances, finding the optimal solution is too time consuming. In this paper, we assess the performance of the MILP formulation when a short time limit is imposed. Moreover, we propose different methods for boosting this performance. We tackle instances representing traffic in the Lille-Flandres station (France), and we show that the boosted MILP formulation achieves very positive results, finding the optimal solution in more than 75% of the experimental runs.

In this paper, we propose a mixed-integer linear programming formulation for tackling a classic problem in railway traffic management. In particular, we deal with perturbed traffic in a complex junction. Here, multiple lines cross allowing train routing and scheduling modifications with respect to the planned timetable. Our formulation exploits alternative routing and scheduling to minimize delay propagation. This formulation allows modelling the infrastructure in terms of track-circuits, that is, with a higher granularity than what is typically done in the literature (where only block sections are modeled). In a thorough experimental analysis, we assess the impact of this granularity. The results indicate that, through the finer granularity, it is possible to achieve a much higher exploitation of the existing capacity.

At peak hours, railway timetables extensively exploit the infrastructure for accommodating traffic. Hence, the occurrence of unexpected events, even of apparently negligible entity, may cause a relevant deviation with respect to the scheduled timetable. If a train is delayed due to an unexpected event, conflicts may emerge, multiple trains may claim the same track section concurrently : in this case trains may have to stop or slow-down for ensuring safety and delay propagation may emerge. The selection of the train routing and scheduling for minimizing delay propagation has been formalized as the real-time Railway Traffic Management Problem (rtRTMP). In this study, we propose a fixed-speed mixed-integer linear programming formulation for optimally solving the rtRTMP. We model the infrastructure in terms of track-circuits, which are the basic components for train detection. In a thorough experimental analysis we quantify the improvement, in terms of reduction of delay propagation, that can be achieved by allowing the platform assignment to be different from the one defined in the timetable.

The real time railway traffic management seeks for the train routing and scheduling that minimize delays after an unexpected event perturbs the operations. In this paper, we propose a mixed-integer linear programming formulation for tackling this problem, modeling the infrastructure in terms of track-circuits, which are the basic components for train detection. This formulation considers all possible alternatives for train rerouting in the infrastructure and all rescheduling alternatives for trains along these routes. To the best of our knowledge, we present the first formulation that solves this problem to optimality. We tested the proposed formulation on real perturbation instances representing traffic in a control area including the Lille Flandres station (France), achieving very good performance in terms of computation time.

Scheduling theory (ST) is a formal framework which have been used many times over the last decade as starting point to model railway traffic management. Graph theory (GT), mathematical programming (MP) or constraint programming (CP) have also be used as additional steps to scheduling theory. This is illustrated in figure 1. The last step is a solution algorithm that is many times based a complete or partial enumeration procedure (EP) of feasible solutions where large subsets are discarded. This article will focus on the path ST → CP → EP to solve railway management problems. I will first briefly introduce each field, and after applications to the railway traffic management.

Scheduling is the process of allocating resources to activities over time. In a scheduling problem, resources are scarce and constrained in various ways (e.g. in the order of activities and the capacity of resources), and one is looking for a schedule of the activities that both satisfies the constraints and is optimal according to some criteria. This paper deals with an extension of a constraint-based scheduling model of real time management of train traffic through junctions and large stations. This extension allows the improvement of the model of conflicts between trains running in opposite directions with the use of state resources. To tackle large instances, a temporal decomposition has been used. We tested the model and algorithms by applying to problem instances of a real terminal station. Computational results show that with the temporal decomposition method we are able to obtain good solution in short computation time