Dynamic and robust timetable rescheduling for uncertain railway disruptions

Zhu, YC Youcai; Goverde, Rob M.P.

doi:10.1016/j.jrtpm.2020.100196

Cited by 23 publications

(17 citation statements)

References 13 publications

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“…Zhu and Goverde (2019b) propose a mixed-integer linear programming model for railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions that also optimizes the short-turning locations depending on the available capacity and generated train delays. Zhu and Goverde (2020a) extend this model into a rolling horizon two-stage stochastic programming problem to deal with uncertainties of disruption durations. Zhu and Goverde (2020b) propose an integrated timetable rescheduling and passenger reassignment model during railway disruptions that extends the models in Zhu and Goverde (2019a, b) towards passenger-oriented timetable rescheduling.…”

Section: Timetable Reschedulingmentioning

confidence: 99%

A next step in disruption management: combining operations research and complexity science

et al. 2021

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Railway systems occasionally get into a state of being out-of-control, meaning that barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing disruption management techniques cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework combining techniques from complexity science and operations research, aiming at reducing the impact of these situations and—if possible—avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations, (ii) isolating a specific region such that delay stops propagating, and (iii) the application of decentralized decision making, more suited for information-sparse out-of-control situations.

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Section: Timetable Reschedulingmentioning

confidence: 99%

A next step in disruption management: combining operations research and complexity science

et al. 2021

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“…Shakibayifar et al [20] proposed a two-stage SP model to cope with stochastic fluctuation of arrival rates in an urban train timetable problem. Considering the uncertainty of a disruption happening in railway operations, Zhu and Goverde [21] formulated and solved a robust timetable rescheduling problem using a rolling horizon two-stage SP method.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…We model the stochastic arrival time of each delayed HSR train at the hub station using Gaussian, Weibull, and uniform probability distributions, which are commonly used in the literature to model train delays [17,21,[32][33][34][35].…”

Section: Scenarios Of the Uncertainty And Computational Timementioning

confidence: 99%

A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

Long

Meng

Luan

et al. 2020

Journal of Advanced Transportation

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The metro system is an important component of the urban transportation system due to the large volume of transported passengers. Hub stations connecting metro and high-speed railway (HSR) networks are particularly critical in this system. When HSR trains are delayed due to a disruption on the HSR network, passengers of these trains arriving at the hub station at night may fail to get their last metro connection. The metro operator can thus decide to schedule extra metro trains at night to serve passengers from delayed HSR trains. In this paper, we consider the extra metro train scheduling problem in which the metro operator decides how many extra metro trains to dispatch and their schedules. The problem is complex because (i) the arrival of delayed HSR trains is usually uncertain, and (ii) the operator has to minimize operating costs (i.e., number of additional trains and operation-ending time) but maximize the number of served passengers, which are two conflicting objectives. In other words, the problem we consider is stochastic and biobjective. We formulate this problem as a two-stage stochastic program with recourse and use an epsilon-constrained method to find a set of nondominated solutions. We perform extensive numerical experiments using realistic instances based on the Beijing metro network and two HSR lines connected to this network. We find that our stochastic model outperforms out-of-sample a deterministic model that relies on forecasts of the delay by a range of 3–5%. Moreover, we show that our solutions are nearly optimal by computing a perfect information dual bound and obtaining average optimality gaps below 1%.

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“…Recent traffic management applications with different objectives or priorities have been reviewed by Corman et al 1,2 TM problems are usually formulated by different mathematical models and solved with various advanced algorithms. Past research includes various TMS for freight rail, 3 passenger rail, [4][5][6] urban rail transit system 7,8 whose objectives are mainly improving the efficiency of multiple aspects of railway operations directly associated with the TMS, and intelligent maintenance system covering the maintenance of railway track, 9 railway asset, 10 entire railway infrastructure, 11 etc., which also minimise the cost of operating the railway and ensure stable daily services.…”

Section: Introductionmentioning

confidence: 99%

Novel approach for validation of innovative modules for railway traffic management systems in a virtual environment

Jin

Ulianov

Hyde

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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To increase operational efficiency, resilience and capacity of the railway system, the development of modern railway traffic management system (TMS) has attracted more and more attention in recent years. To support the development and implementation of the next generation of TMS and related applications, advanced data collection, transmission and processing approaches, digitalised databases, and virtual validation platforms, etc., are required. In the context of the TMS development (addressed by Technology Demonstrator 2.9 of Shift2Rail Innovation Programme 2), this support is to be provided by a scalable, interoperable and standardised communication platform for internal and external communication between different subsystems, applications and clients. This paper outlines the approach of the ongoing OPTIMA project aimed to develop a communication platform demonstrator for railway TMS based on a novel Integration Layer (IL) and its various interfaces to entities including integration layer services, TMS service, rail business service, external services and operator workstations. Further detailed discussion in this paper relates to the approach to validating the communication platform demonstrator as a functional entity, and as a virtual testing environment to validate railway traffic management and other applications. The validation approach for the applications tested on the communication platform demonstrator is also presented. The results of future implementation of this validation approach will be used to assess the functionality of the communications platform demonstrator developed, and the initial TMS applications tested on it, and form an important step towards developing and implementing IL based communications platforms for future TMSs.

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Dynamic and robust timetable rescheduling for uncertain railway disruptions

Cited by 23 publications

References 13 publications

A next step in disruption management: combining operations research and complexity science

A next step in disruption management: combining operations research and complexity science

A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

Novel approach for validation of innovative modules for railway traffic management systems in a virtual environment

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