23 research outputs found

    Capacity and reliability on railway networks: a simulative approach

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    2008/2009An efficient train operation is a primary success factor for all infrastructure managers, since it allows operating a higher number of trains without significant infrastructure investments. As known, a trade-off exists between capacity and punctuality, forcing planners to find an equilibrium allowing the highest number of slots to be operated with satisfying punctuality indicators. This is particularly challenging in nodes, where the combination of different stochastic parameters on various lines and for different trains dramatically increases modelling tasks. In the last years, railway simulators have become a very powerful instrument to support the different steps of the planning process: from the layout design to capacity investigations and offer model validations. More recently, the possibility of an automatic import of infrastructure layouts and timetables widened the application spectrum of micro-simulators to large nodes and to more detailed stochastic stability evaluations. Stochastic micro-simulators can reproduce most processes involved in rail traffic and comprehend not only its deterministic aspects, but also human factors. This is particularly relevant in order to simulate traffic under realistic conditions, considering variability at border, various driving styles and stop times. All these parameters have to be calibrated using real-world collected data for single trains or train families, considering their different behaviour in the network and at its border. Since a perfect representation of all stochastic and deterministic parameters involved in rail traffic is not possible, a calibrated model must be validated to evaluate its precision before using it in practice. Calibration has been tested on the Palermo - Punta Raisi single-track line, on the Trieste - Venice double-track line and in the node of Turin. The model is first used to forecast reliability of the operations after infrastructure and timetable changes. Results have been compared ex-post with real traffic data, showing remarkable reliability. An approach is then presented, in which stochastic micro-simulation is used to represent the relationship between robustness, capacity and a number of other important factors, such as traffic variability or running time supplements. The approach can be used to estimate the buffer times, and the running time supplements to obtain a given reliability level. First, micro simulation with its advantages and weaknesses is presented; then, after a presentation of the most common reliability measures, the a new indicator is explained. Third, calibration, validation and application of the case studies is described; in the last part, an approach to evaluate the trade-off between different parameters is presented.La domanda di trasporto collettivo, in particolare ferroviario, è fortemente cresciuta nelle grandi aree urbane anche per effetto di specifici strumenti pianificatori volti a favorire l’utilizzo di alternative sostenibili sotto il profilo ambientale, nel rispetto della configurazione del territorio. La conseguente saturazione dei nodi e delle linee e la contemporanea necessità di aumentare la regolarità del servizio offerto impongono ai gestori un deciso aumento nella precisione della pianificazione dell'esercizio. Come è noto, esiste un trade-off tra capacità e regolarità dei servizi ferroviari, che obbliga i pianificatori a trovare un equilibrio, massimizzando il numero di treni e garantendo nel contempo un soddisfacente livello di regolarità. Il mantenimento di tale equilibrio risulta particolarmente complesso nei nodi, dove la combinazione di fenomeni stocastici in numerose linee e per diversi servizi aumenta notevolmente le difficoltà di modellizzazione della circolazione reale. Negli ultimi anni, gli strumenti di microsimulazione della circolazione ferroviaria sono divenuti uno strumento potente a supporto delle diverse fasi di pianificazione, dalla progettazione funzionale dell’infrastruttura alle stime di capacità e alla validazione degli orari. Più recentemente, la possibilità di ottenere l'importazione automatica del modello infrastrutturale e degli orari ha ampliato lo spettro di applicazione della microsimulazione ai nodi complessi ed alle valutazioni ex-ante della stabilità degli orari. La microsimulazione consente di riprodurre la gran parte dei processi coinvolti nella circolazione ferroviaria, comprendendo non solo i suoi aspetti deterministici, ma anche il fattore umano. Ciò risulta particolarmente importante al fine di simulare la circolazione in condizioni reali, considerando i ritardi in ingresso nell’area di simulazione, i diversi stili di guida e la variabilità dei tempi di fermata nelle stazioni. Tali parametri necessitano tuttavia di un’adeguata calibrazione mediante dati reali per singoli servizi o famiglie di treni, considerando le diversità di comportamento all’interno della rete simulata ed al cordone. Dato che non è possibile una rappresentazione perfetta di tutti i parametri stocastici e deterministici che definiscono la circolazione ferroviaria, un modello calibrato deve essere validato per valutarne la precisione, prima di venire utilizzato in pratica. La calibrazione è stata testata sulla linea a semplice binario Palermo-Punta Raisi, sulla Venezia - Trieste, a doppio binario e nel nodo di Torino. In primis, il modello è stato utilizzato per la stima ex-ante della regolarità in seguito a modifiche infrastrutturali e di orario. Dopo l’attivazione dei provvedimenti simulati, i dati della circolazione reale sono stati confrontati con quelli simulati, dimostrando la notevole attendibilità della stima. E’ stata quindi sviluppata una metodologia, in cui la microsimulazione viene utilizzata per rappresentare la relazione tra capacità, regolarità dei servizi ed una serie di altri fattori, quali ad esempio gli allungamenti sul tempo di percorrenza e i fenomeni stocastici. L’approccio può essere utilizzato per la stima delle riserve e degli allungamenti da inserire nell’orario per ottenere una data regolarità. La tesi muove dalla presentazione della microsimulazione, delle sue potenzialità e dei suoi limiti; quindi, dopo la descrizione delle principali misure di regolarità, viene introdotto un nuovo indicatore. Seguono la presentazione delle metodologie di calibrazione e validazione di un modello di microsimulazione e la loro applicazione ai casi di studio. Viene infine presentata la metodologia per la stima coordinata di regolarità e capacità, le cui potenzialità sono illustrate nei casi di studio.XXII Ciclo198

    Disruption management in passenger railway transportation.

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    This paper deals with disruption management in passengerrailway transportation. In the disruption management process, manyactors belonging to different organizations play a role. In this paperwe therefore describe the process itself and the roles of thedifferent actors.Furthermore, we discuss the three main subproblems in railwaydisruption management: timetable adjustment, and rolling stock andcrew re-scheduling. Next to a general description of these problems,we give an overview of the existing literature and we present somedetails of the specific situations at DSB S-tog and NS. These arethe railway operators in the suburban area of Copenhagen, Denmark,and on the main railway lines in the Netherlands, respectively.Since not much research has been carried out yet on OperationsResearch models for disruption management in the railway context,models and techniques that have been developed for related problemsin the airline world are discussed as well.Finally, we address the integration of the re-scheduling processesof the timetable, and the resources rolling stock and crew.

    Methods to estimate railway capacity and passenger delays

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    An iterative heuristic for passenger-centric train timetabling with integrated adaption times

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    In this paper we present a method to construct a periodic timetable from a tactical planning perspective. We aim at constructing a timetable that is feasible with respect to infrastructure constraints and minimizes average perceived passenger travel time. In addition to in-train and transfer times, our notion of perceived passenger time includes the adaption time (waiting time at the origin station). Adaption time minimization allows us to avoid strict frequency regularity constraints and, at the same time, to ensure regular connections between passengers’ origins and destinations. The combination of adaption time minimization and infrastructure constraints satisfaction makes the problem very challenging. The described periodic timetabling problem can be modelled as an extension of a Peri- odic Event Scheduling Problem (PESP) formulation, but requires huge computing times if it is directly solved by a general-purpose solver for instances of realistic size. In this paper, we propose a heuristic approach consisting of two phases that are executed iteratively. First, we solve a mixed-integer linear program to determine an ideal timetable that mini- mizes the average perceived passenger travel time but neglects infrastructure constraints. Then, a Lagrangian-based heuristic makes the timetable feasible with respect to infras- tructure constraints by modifying train departure and arrival times as little as possible. The obtained feasible timetable is then evaluated to compute the resulting average per- ceived passenger travel time, and a feedback is sent to the Lagrangian-based heuristic so as to possibly improve the obtained timetable from the passenger perspective, while still respecting infrastructure constraints. We illustrate the proposed iterative heuristic approach on real-life instances of Netherlands Railways and compare it to a benchmark approach, showing that it finds a feasible timetable very close to the ideal one

    Disruption Management in Passenger Railway Transportation

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    This paper deals with disruption management in passenger railway transportation. In the disruption management process, many actors belonging to different organizations play a role. In this paper we therefore describe the process itself and the roles of the different actors. Furthermore, we discuss the three main subproblems in railway disruption management: timetable adjustment, and rolling stock and crew re-scheduling. Next to a general description of these problems, we give an overview of the existing literature and we present some details of the specific situations at DSB S-tog and NS. These are the railway operators in the suburban area of Copenhagen, Denmark, and on the main railway lines in the Netherlands, respectively. Since not much research has been carried out yet on Operations Research models for disruption management in the railway context, models and techniques that have been developed for related problems in the airline world are discussed as well. Finally, we address the integration of the re-scheduling processes of the timetable, and the resources rolling stock and crew

    Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

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    Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

    Disruption Management in Passenger Railways

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