Numerous studies have explored modelling approaches for managing disruption in rail networks, yet the examples of practical applications remain somewhat limited. This paper introduces a real-time, practical predictive modelling approach to simulate rail network operations during disruptions. In the United Kingdom, the rail network is highly congested, with the train services closely interconnected. As a result, disruptions can easily propagate the effects of delays across the entire network. This approach aims to simulate the propagation of delay, analyse network interdependencies, and predict rail network performance during disruptions. The results indicate a good match between the in-field data and the results of the simulation, illustrating the potential of the model to produce accurate estimations. This model has the potential to serve as a real-time advisory tool for managing disruptions in a highly congested rail network, swiftly producing reliable estimates of the outcomes for different service alterations before introduction.
FaramehrSHemidaHFujiyamaT. Evaluation of the impact of urban water systems on railways: the scenario of track flooding caused by a water main burst. Proc Inst Mech Eng F J Rail Rapid Transit2020; 234: 351–358.
DaviesBAndrewsJ. The impact of summer heatwaves on railway track geometry maintenance. Proc Inst Mech Eng F J Rail Rapid Transit2021; 235: 1158–1171.
4.
DobneyKBakerCJChapmanL, et al.The future cost to the United Kingdom’s railway network of heat-related delays and buckles caused by the predicted increase in high summer temperatures owing to climate change. Proc Inst Mech Eng F J Rail Rapid Transit2010; 224: 25–34.
Rail SafetyStandards Boards, Network Rail. Why do we need a Rail Technical Strategy?. Rail technical strategy. https://railtechnicalstrategy.co.uk/about/(accessed 15 August 2024).
7.
CareyMKwiecińskiA. Stochastic approximation to the effects of headways on knock-on delays of trains. Transportation Research Part B: Methodological. 1994; 28: 251–267.
DekkerMMvan LieshoutRNBallRC, et al.A next step in disruption management: combining operations research and complexity science. Public Transp2022; 14: 5–26.
10.
VavoneTvdBBlenkersL, et al.An MPC-based rescheduling algorithm for disruptions and disturbances in large scale railway network. IEEE Trans Autom Sci Eng2022; 19: 99–113.
11.
DollevoetDHKroonLG, et al.Aplication of an iterative framework for real-time railway. Comput Oper Res2017; 78: 203–217.
12.
CiufudeanCBuzdugaC. Discrete event models for railway traffic control. AIP Conf Proc. 2022; 2570: 40010.
13.
Adjetey-BahunKBirregahBChâteletE, et al.A model to quantify the resilience of mass railway transportation systems. Reliab Eng Syst Saf2016; 153: 1–14.
14.
WangDZWLiuHSzetoWY, et al.Identification of critical combination of vulnerable links in transportation networks – a global optimisation approach. Transportmetrica: Transp Sci2016; 12: 346–365.
15.
SpanningerTTrivellaABüchelB, et al.A review of train delay prediction approaches. J Rail Transp Plan; 22:100312. DOI: 10.1016/j.jrtpm.2022.100312. Epub ahead of print 1 June 2022.
16.
HwangC-CLiuJ-RStudentPD. A Simulation Model for Estimating Knock-on Delay of Taiwan Regional Railway. Journal of the Eastern Asia Society for Transportation Studies. 2010; 8: 1110–1125.
17.
LutebergetBClaessenKJohansenC, et al.SAT modulo discrete event simulation applied to railway design capacity analysis. Formal Methods Syst Des2021; 57: 211–245.
18.
AlmodóvarMGarcía-RódenasR. On-line reschedule optimization for passenger railways in case of emergencies. Comput Oper Res2013; 40: 725–736.
19.
YangL-XLiXiang. A control simulation method of high-speed trains on railway network with irregular influence. Commun Theor Phys. 2011; 56: 411.
20.
KecmanPGoverdeRMP. Online data-driven adaptive prediction of train event times. IEEE Trans Intell Transport Syst2015; 16: 465–474.
21.
LamCYTaiK. Network topological approach to modeling accident causations and characteristics: analysis of railway incidents in Japan. Reliab Eng Syst Saf; 193:106626. DOI: 10.1016/j.ress.2019.106626, Epub ahead of print 1 January 2020.
22.
LessanJFuLWenC. A hybrid Bayesian network model for predicting delays in train operations. Comput Ind Eng2019; 127: 1214–1222.
23.
ZhangLFengXDingC, et al.Mitigating errors of predicted delays of a train at neighbouring stops. IET Intell Transp Syst2020; 14: 873–879.
24.
ZhuangHFengLWenC, et al.High-speed railway train timetable conflict prediction based on fuzzy temporal knowledge reasoning. Engineering2016; 2: 366–373.
25.
Galvão ScheideggerAPFernandes PereiraTMoura de OliveiraML, et al.An introductory guide for hybrid simulation modelers on the primary simulation methods in industrial engineering identified through a systematic review of the literature. Comput Ind Eng2018; 124: 474–492.
26.
WangPZhangQP. Train delay analysis and prediction based on big data fusion. Transportation Safety and Environment2019; 1: 79–88.
27.
WenCMouWHuangP, et al.A predictive model of train delays on a railway line. J Forecast2020; 39: 470–488.
MarcusC.T1154 enabling better planning and resource management during disruption. Report, Rail Safety and Standards Board, 2021.
30.
Rail Safety and Standards Board, Network Rail, UKRRIN. Rail technical strategy. Report, Rail Safety and Standards Board, 2024.
31.
AndyC.Enabling better planning and resource management during disruption. Report, Rail Safety and Standards Board, 2020.
32.
IlalokhoinOPantRHallJW. A multi-track rail model for estimating journey impacts from extreme weather events: a case study of Great Britain’s rail network. Int J Real Ther2022; 10: 133–158.
33.
Tan KangRuiRasa Remenyte-PrescottDarren Prescott, et al. Analysing railway network interdependencies: simulation of reactionary delay and automated rescheduling. Part 8 complex systems and critical infrastructures business continuity modelling and optimization & reliability, safety and security related management systems. Polish Safety and reliability Association, pp. 149–158.
García del ValleEPLagunes GarcíaGPrieto SantamaríaL, et al.Disease networks and their contribution to disease understanding: a review of their evolution, techniques and data sources. J Biomed Inf; 94:103206. DOI: 10.1016/j.jbi.2019.103206. Epub ahead of print 1 June 2019.
36.
LuQC. Modeling network resilience of rail transit under operational incidents. Transp Res Part A Policy Pract2018; 117: 227–237.
37.
MeesitRAndrewsJ. Ranking the critical sections of railway networks. Proc Inst Mech Eng F J Rail Rapid Transit2021; 235: 361–376.
LawAM. Advantages, disadvantages and pitfalls of simulation. Simulation modeling and analysis. 5th ed.. McGraw-Hill Education, 2015, pp. 72–73. https://www.averill-law.com.
SoginSLLaiYCDickCT, et al.Analyzing the transition from single- to double-track railway lines with nonlinear regression analysis. Proc Inst Mech Eng F J Rail Rapid Transit2016; 230: 1877–1889.
43.
HydeJWilkinsonHSolutionsR, et al.Rail performance modelling for strategic decision making-final report (COF-DSP-05) data Sandbox+. Report, Rail Safety and Standards Board, 2021.
