Statistical modelling of delays in a rail freight transportation network

Barta, János; Rizzoli, Andrea Emilio; Salani, Matteo; Gambardella, Luca Maria

doi:10.1109/wsc.2012.6465188

Cited by 21 publications

(13 citation statements)

References 14 publications

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“…Barta et al [103] developed a Markov chain model to evaluate the evolution of freight train delays at their successive terminals and classify terminals in terms of the roles of the trains. Şahin [26] established a Markov chain model to illustrate delay propagation and recovery using the observed historical data collected from a single-track line of the Turkish State railways.…”

Section: B: Gmmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study. INDEX TERMS Data-driven, delay distribution, delay propagation, timetable rescheduling, train dispatching, machine learning.

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Section: B: Gmmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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“…More adequate in such conditions of a railway network functioning is application of the macrolevel approach to devising stochastic models of delay propagations. For example, in [9] a stochastic mathematical model based on the Markov chain was proposed to simulate the propagation of cargo train delays taking into account the ability of intermodal terminals to absorb delays occurring in the network.…”

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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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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“…Other train scheduling problems consider operational guidance for handling disruptions to planned schedules (Barta et al 2012;D'Ariano et al 2007). Researchers have introduced 23 models for assigning locomotives to trains (Vaidyanathan et al 2008), crew planning (Chahar et al 2011), scheduling maintenance (Budai et al 2006), and managing empty railcar movements (Sherali and Suharko 1998).…”

Section: Locomotive Research Applicationsmentioning

confidence: 99%

Integrated optimization and simulation models for the locomotive refueling system configuration problem

Verschelden¹,

Stamm²,

Easton³

2017

2017 Winter Simulation Conference (WSC)

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Locomotives in the U.S. use over 3 billion gallons of fuel each year and faster refueling can increase rail network capacity without the infrastructure cost associated with new terminals or tracks. This thesis introduces the locomotive refueling system configuration problem (LRSCP), which seeks to improve efficiency in refueling yards through new technologies or policies. This research also creates two new methods to solve LRSCP. Based on experimental results, solving either TRIP or SLIP and incorporating the optimal refueling policy improves railyard operations by 10 to 30%. This impact is statistically significant and increases the capacity of a railyard. Additionally, it impacts other important parameters such as time spent in the yard and the maximum queue for the railyard. Furthermore, there is a significant decrease in wasted time and an improvement to railyard efficiency.Implementing either method should increase a railyard's capacity and significantly increase revenue opportunities.iv

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Statistical modelling of delays in a rail freight transportation network

Cited by 21 publications

References 14 publications

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

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

Integrated optimization and simulation models for the locomotive refueling system configuration problem

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