Stochastic modelling of delay propagation in large networks

Büker, Thorsten; Seybold, Bernhard

doi:10.1016/j.jrtpm.2012.10.001

Cited by 92 publications

(71 citation statements)

References 6 publications

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“…The stochastic model which comes really close to our approach is in Büker and Seybold [2]. They basically have the same form of distribution of the train arrivals and respective waiting policies.…”

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confidence: 66%

Stochastic model for the real-time train rescheduling

Davydov¹,

Chebotarev²,

Kablukova³

2017

Int. J. TDI

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The article explores the problem of train rescheduling based on the actual situation. The proposed stochastic model uses specific distributions of operating times which are dependent on the current traffic conditions. The arrival time distribution is considered as a result of adjusting the train trajectory by speed control. The results of modelled arrival distributions correspond well with the experimental data received at the russian railways. The proposed model is used for prevention of sequence-of-trains conflicts and violations of connections. The basis of deviation prediction is two-train model of mesalevel which uses actual features of scattering of the operation times both at sites and at stations. The article also proposed a new measure of arrival delay which considers the share of satisfied passengers.

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confidence: 66%

Stochastic model for the real-time train rescheduling

Davydov¹,

Chebotarev²,

Kablukova³

2017

Int. J. TDI

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“…Cadarso and Á ngel [17] presented an approach to optimize timetable and rolling stock assignment with special consideration of passenger demand under large-scale disruptions in rapid transit network. Delay propagation models help to combine micro-propagation model with mathematical statistics to predict and estimate the service performance and transport efficiency, and max-plus algebra [18] and stochastic distribution [19] are often used in this kind of research. In the max-plus algebra, by viewing the operation plan under periodic timetable as a discrete event dynamic system, a recursion function considering buffer time and recovery time can be formulated to describe the train operating status and further reveal the time-space propagation mechanism.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Zeng

2018

Urban Rail Transit

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Simulating the congestion propagation of urban rail transit system is challenging, especially under oversaturated conditions. This paper presents a congestion propagation model based on SIR (susceptible, infected, recovered) epidemic model for capturing the congestion prorogation process through formalizing the propagation by a congestion susceptibility recovery process. In addition, as congestion propagation is the key parameter in the congestion propagation model, a model for calculating congestion propagation rate is constructed. A gray system model is also introduced to quantify the propagation rate under the joint effect of six influential factors: passenger flow, train headway, passenger transfer convenience, time of congestion occurring, initial congested station and station capacity. A numerical example is used to illustrate the congestion propagation process and to demonstrate the improvements after taking corresponding measures.

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“…rail operation) presents a relationship of reciprocal influence with dwell times at stations, which are key factors in order to optimise the reliability of rail systems [36][37][38][39]. Dwell times play an important role especially in propagating delays [40][41][42][43]. Indeed, when a first delay occurs, if the timetable is neither stable nor flexible enough, the perturbation propagates and generates secondary delays, reducing service quality and hence passenger satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Determining Dwell Times Consistent with Passenger Flows in the Case of Metro Services

et al. 2017

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The importance of a mobility system based on railway technology as the backbone of public transport is now widely acknowledged. Indeed, rail systems are green, high performing, smart and able to ensure a high degree of safety. Therefore, modal split should be steered towards rail transport by increasing the attractiveness of this transport mode. In this context, a key element is represented by the timetabling design phase, which must aim to guarantee an appropriate degree of robustness of rail operations in order to ensure a high degree of system reliability and increase service quality. A crucial factor in the task of timetabling entails evaluating dwell times at stations. The innovative feature of this paper is the analytical definition of dwell times as flow dependent. Our proposal is based on estimating dwell times according to the crowding level at platforms and related interaction between passengers and the rail service in terms of user behaviour when a train arrives. An application in the case of a real metro system is provided in order to show the feasibility of the proposed approach.

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Stochastic modelling of delay propagation in large networks

Cited by 92 publications

References 6 publications

Stochastic model for the real-time train rescheduling

Stochastic model for the real-time train rescheduling

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Methodology for Determining Dwell Times Consistent with Passenger Flows in the Case of Metro Services

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