Network effects in railways

Landex, Alex

doi:10.2495/cr120331

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…The advantage of the method of modelling the propagation of train delays is the possibility of its use on branched railway tracks with acceptable speed and accuracy. This allows taking into account the impact of the 'network effect' [15]. The application of this approach will automate the complex process of finding rational values of compensation time in the threads of trains of different categories in a railway section and, as a consequence, increase the punctuality and reliability of regulatory train schedules.…”

Section: Discussion Of the Results Of The Research On Modelling The Propagation Of Train Delays At A Branched Railway Test Sitementioning

confidence: 99%

“…Based on the conditions of the object under study, it is proposed to apply a macroscopic approach to modelling the propagation of train delays. This approach can more adequately describe the uncertainty of the parameters in the train traffic system without following the schedule in the network, taking into account the topological properties of the network and the impact of the so-called 'network effect' [15]. Based on the development of methods for analysing complex networks, there have been studies aimed at formalizing the dynamics of the propagation of train delays on topologies of large networks [16,17].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Development of a method for modelling delay propagation in railway networks using epidemiological SIR models

Gurin

Prokhorchenko

Kravchenko

et al. 2020

EEJET

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A method has been developed to simulate propagation of train delays in branched railroad ranges using modified epidemiological SIR models. These models take into account the mutual influence of trains with different priorities in the flow. This makes it possible to study the heterogeneous dynamics in the propagation of delays among trains of different priorities. To consider the propagation of the primary delay in space and time, it is proposed to represent the topology of the railway network in the form of an undirected graph with reference to the edge of the graph in the mathematical system of differential equations of the SIR model. This unifies the process of constructing SIR models for each edge (section) of the network graph and reduces the dimension of the problem. To take into account the influence of the "network effect", it is proposed to determine the transit coefficient for each station of the section. This coefficient helps calculate the number of delayed trains for adjacent sections. To set SIR models, it is proposed to use empirical data on the propagation of the average delay in the standard traffic schedule in the corresponding section. For the sequential solution of SIR models corresponding to interconnected network sections, an algorithm is applied to turn the network graph into a directed tree the root of which is the station where the delay occurs. Tests on modelling the propagation of train delays in the railway network are carried out taking into account the mutual influence of different categories of trains in the flow and the built-in time reserves for the restoration of movement. The obtained simulation results have confirmed the adequacy of the solutions and helped quantify the influence of primary delays and the amount of time reserve in the schedules of trains of various categories on the reliability of the standard train schedule

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Section: Discussion Of the Results Of The Research On Modelling The Propagation Of Train Delays At A Branched Railway Test Sitementioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a method for modelling delay propagation in railway networks using epidemiological SIR models

Gurin

Prokhorchenko

Kravchenko

et al. 2020

EEJET

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“…In reality, the observed capacity could be lower given network-intrinsic constraints arising from the harmonisation of train paths with adjacent line segments. these so-called network effects (see [30]) are not considered in the present study.…”

Section: Methodical Approachmentioning

confidence: 99%

Impact of faster freight trains on railway capacity and operational quality

Geischberger¹,

Moensters²

2020

Int. J. TDI

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Impact of faster freIght traIns on raIlway capacIty and operatIonal qualIty Jakob geIschberger & mIchael moensters Institute of transportation systems (ts), germany abstract In view of the transition towards climate-friendly transportation, mobility and freight traffic demand are both expected to grow in europe's already heavily occupied railway networks. to accommodate the increasing traffic volume, capacity-optimised traffic planning is a requirement. one key factor in this context is the heterogeneity of train speeds on heavily used mixed-traffic railway lines. In this paper, we study the effects of train speed harmonisation based on the increase of freight train velocities. two planning scenarios are addressed: in the first case, freight trains have a conventional speed profile but can occasionally make use of their higher allowed speed level when needed. this can be used in operations to recover from delays or blend in with passenger traffic. In the second case, freight trains are scheduled to so-called 'express train paths'-reaching a more homogeneous timetable profile. the two planning concepts are analysed and compared in a microscopic simulation approach. It is shown that scenarios based on the first planning concept tend to have positive effects on operational quality and capacity, whereas scenarios using 'express freight train paths' are less promising in this respect.

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“…Its widespread use has given rise to a productive exchange about best practices for using the method, most notably with respect to the decomposition rules and the specification of station areas. In particular, it has been argued that , due to negligence of so-called network effects, larger and consistent observation areas are preferable, as a small-scale segmentation yields an unrealistically small capacity usage (Landex et al, 2006;Landex, 2012).…”

Section: Introductionmentioning

confidence: 99%