Railway network resilience

Meesit, Ratthaphong (2019) Railway network resilience. PhD thesis, University of Nottingham.

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Railways are currently struggling with a growing number of threats such as aging infrastructures, climate changes and deliberate acts (e.g. terrorism). Ensuring the resilience of railway networks thus becomes one of the most challenging and critical issues which needs addressing in order to maintain major services and serve public demand during disruptions. This study aims to refine resilience in the context of railway networks and to develop a resilience model that can be applied to support a decision-making process for establishing a suitable prevention strategy and for implementing an optimal mitigation solution to maximise the resilience of a railway network.

To achieve the aim, this study provides an in-depth review of the concept of resilience and the current state-of-the-art resilience models, not only for railway networks, but also for other critical infrastructure networks such as utilities and telecommunications. The network characteristics and the key performance indicators (KPIs) for each network type are described. Then, the strengths and limitations in the current models are discussed in order to identify the gaps in knowledge and research directions to develop a railway network resilience model.

After the reviewing process, a novel railway network resilience model is proposed based on two main resilience dimensions: robustness and recoverability. The model is developed using a stochastic-discrete event simulation technique, and it consists of three sub-models: railway network performance model, robustness improvement analysis model and recoverability analysis model. The first sub-model is a basis for the other sub-models. It is used to predict the resilience performance of the railway network during both small and large impact disruptions. The framework of this model includes modules for: railway network modelling, passenger modelling, and disruption scenario modelling. The key performance indicators predicted are train service delays, train service cancellations, passenger delays and passenger journey cancellations.

The robustness improvement analysis model applies the first sub-model to identify the critical sections of a railway network. The model considers the type and the likelihood of disruptive events. Thus, the risk-based criticality of network sections can be evaluated. This capability gives infrastructure managers a better solution for railway network robustness improvements.

Last but not least, the recoverability analysis model aims to evaluate the efficiency of different mitigation solutions implemented to increase the resilience of a railway network during track blockage situations (both planned and unplanned disruptions). Two mitigation strategies are considered: short-turning operations and providing rail replacement bus services. Various operating parameters related to these strategies, such as short-turning factors, bus replacement routes, the availability of buses, service frequency, service capacity and operation period, are taken into account. These parameters are then optimised using a Genetic Algorithm in order to obtain the optimal mitigation solutions that result in the lowest operational cost and the highest resilience for a particular disruption.

Finally, an application of the proposed model is demonstrated using the Liverpool railway network in the UK. The simulation example of each sub-model is performed. Then, the results obtained are analysed and transformed into resilience improvement strategies that can be apply in the real-world.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Andrews, John
Prescott, Darren
Keywords: Resilience, Vulnerability analysis, Short-turning operations, Rail replacement bus services, Railway network simulation
Subjects: T Technology > TF Railroad engineering and operation
Faculties/Schools: UK Campuses > Faculty of Engineering
Item ID: 57060
Depositing User: Meesit, Ratthaphong
Date Deposited: 25 Sep 2023 08:17
Last Modified: 25 Sep 2023 08:17
URI: https://eprints.nottingham.ac.uk/id/eprint/57060

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