Design of a Railway Scheduling Model for Dense Services

Caimi, Gabrio; Burkolter, Dan; Herrmann, Thomas; Chudak, Fabián A.; Laumanns, Marco

doi:10.1007/s11067-008-9091-6

Cited by 38 publications

(22 citation statements)

References 12 publications

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“…Caimi et al [3] solve ordering and routing problems in station areas simultaneously, by building a large conflict graph that takes into account multiple scheduling possibilities for each train. A fixed point iteration algorithm has been implemented to compute a feasible solution in a reasonable computation time.…”

Section: Related Literaturementioning

confidence: 99%

Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area

Corman

D’Ariano

Pranzo

et al. 2011

Transportation Planning and Technology

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Railway traffic experiences disturbances during operations while dispatchers need actions to restore feasibility and limit spreading of delays through the network. To help the dispatcher in such task, the dispatching support tool ROMA (Railway traffic Optimization by Means of Alternative graphs) has been designed and implemented. We report on enhancements to the underlaying train dispatching model as well as to the solving algorithms studied in order to tackle the increased complexity of busy stations with multiple conflicting paths and high service frequency. The system is compared with straightforward rules and the current approach in the Netherlands. Extensive computational studies based on accepted statistical distributions of delays assess the effectiveness of the ROMA tool.

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Section: Related Literaturementioning

confidence: 99%

Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area

Corman

D’Ariano

Pranzo

et al. 2011

Transportation Planning and Technology

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“…A recent top-down approach to timetabling is proposed by Caimi et al (2009). With this approach, train service intentions are first generated at the higher level and then a detailed network-wide timetable is built at the lower level by decomposing the network in condensation and compensation zones.…”

Section: Overview Of the Related Literaturementioning

confidence: 99%

Centralized versus distributed systems to reschedule trains in two dispatching areas

et al. 2010

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Railway dispatchers are in charge of rescheduling trains during operations in order to limit propagation of disturbances occurring in real-time. To help the dispatchers in such task, an advanced decision support system, ROMA (Railway traffic Optimization by Means of Alternative graphs), has been recently implemented to optimize railway traffic within a single dispatching area. This paper presents a novel distributed optimization system to control trains running in a Dutch railway network that is divided into two complex dispatching areas with dense traffic, each one controlled by a single dispatcher with the support of a local ROMA. A coordination level is introduced in order to manage the interaction among the two local ROMAs. An extensive computational assessment of the centralized and distributed systems is performed by using simple and advanced train scheduling algorithms, including dispatching rules adopted during operations. The effectiveness of the distributed system is shown in terms of computation time and delay minimization for practical statistical entrance delay distributions and in presence of an increasing number of blocked platforms in the main station area.

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“…Usually, lines correspond to the first one, while junctions, stations and other bottleneck areas correspond to the latter, e.g. [10,11].…”

Section: Definitions Of Macroscopic and Microscopic Representationsmentioning

confidence: 99%

A multi scalable model based on a connexity graph representation

Gély

Dessagne

Pesneau

et al. 2010

WIT Transactions on the Built Environment

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Train operations will be greatly enhanced with the development of new decision support systems. However, when considering problems such as online rescheduling of trains, experience shows a pitfall in the communication between the different elements that compose them, namely simulation software (in charge of projection, conflict detection, validation) and optimization tools (in charge of scheduling and decision making). The main problem is the inadequacy of the infrastructure's monolithic description and the inability to manage together different description levels.Simulation uses a very precise description, while the optimization of a mathematical problem usually does not. Indeed, an exhaustive description of the whole network is usually counter-productive in optimization problems; the description must be accurate, but should rely on a less precise representation. Unfortunately, the usual model representing the railway system does not guarantee compatibility between two different description levels; a representation usually corresponds to a given (unique) description level, designed in most cases with a specific application in mind, such as platforming. Moreover, further modifications that could improve performances or precision are usually impossible.We propose, therefore, a model with a new description of the infrastructure that permits one to scroll between different description levels. These operations can be automated via dynamic aggregation and disaggregation methods. They allow one to manage heterogeneous descriptions and cooperation between various tools using different description levels.This model is based on the connexity graph representation of the infrastructure resources. We will present how to generate corresponding mathematical models based on resource occupancy and will show how the aggregation of resources leads to the aggregation of properties (e.g. capacity) that can be translated into mathematical constraints in the optimization problem.

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Design of a Railway Scheduling Model for Dense Services

Cited by 38 publications

References 12 publications

Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area

Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area

Centralized versus distributed systems to reschedule trains in two dispatching areas

A multi scalable model based on a connexity graph representation

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