Dealing with Stochastic Dependence in the Modeling of Train Delays and Delay Propagation

Yuan, Jianxin

doi:10.1061/40932(246)641

Cited by 42 publications

(58 citation statements)

References 8 publications

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“…In Denmark it has been decided that the capacity consumption of a railway line is equal to the highest capacity consumption of the line sections, cf. Figure 11 (this assumption is also made by [1], [8] and [11]). This is because most trains operate on more than one line section, and the line section that is occupied for the most time will (generally) be limiting for operating more trains.…”

Section: Analyzing Line Sections With Single Track Double Track and mentioning

confidence: 72%

Capacity measurement with the UIC 406 capacity method

Landex

Schittenhelm

Kaas³

et al. 2008

WIT Transactions on the Built Environment

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This article describes the fast and effective UIC 406 method for calculating capacity consumption on railway lines. It is possible to expound the UIC 406 method in different ways which can lead to different capacity consumptions. Therefore, this article describes how the methodology is expounded in Denmark. This includes how and where to divide the railway lines into line sections, how to analyze stations and junctions, and how to examine line sections with different amounts of tracks.

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Section: Analyzing Line Sections With Single Track Double Track and mentioning

confidence: 72%

Capacity measurement with the UIC 406 capacity method

Landex

Schittenhelm

Kaas³

et al. 2008

WIT Transactions on the Built Environment

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“…Weibull distributions are used to characterize the variability of relevant process times, as in [7,28]. Specifically, the same dataset of events is used as in [7], that contains about 33000 records of arrival and departure events at Utrecht station, corresponding to a month of operations.…”

Section: Distribution Of Process Timesmentioning

confidence: 99%

Susceptibility of optimal train schedules to stochastic disturbances of process times

et al. 2013

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This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various scheduling algorithms to variations in process times (running and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced scheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact on the scheduler performance.

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“…For the given timetable, we consider more than 33000 train events (arrivals, departures, dwell processes and passing times) that have been recorded at Utrecht Central station in April 2008 by ProRail. A statistical fitting procedure (Yuan 2006) is adopted to set the parameters of the Weibull distributions, which are used to characterize the delays of different trains and the variation in the dwell time process.…”

Section: Description Of the Instancesmentioning

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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Dealing with Stochastic Dependence in the Modeling of Train Delays and Delay Propagation

Cited by 42 publications

References 8 publications

Capacity measurement with the UIC 406 capacity method

Capacity measurement with the UIC 406 capacity method

Susceptibility of optimal train schedules to stochastic disturbances of process times

Centralized versus distributed systems to reschedule trains in two dispatching areas

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