A model and methodology for resilience assessment of interdependent rail networks – Case study of Great Britain's rail network. (January 2023)

• Record Type: Journal Article
• Authors: Ilalokhoin, Ohis; Pant, Raghav; Hall, Jim W.
• Is Part Of: Reliability engineering & system safety. Volume 229(2023)
• Publication Date: 2023-01
• Subjects: Risk -- Resilience -- Critical infrastructure systems -- Rail networks -- Systems-of-systems -- Interdependencies -- Rail disruptions -- Hazards -- Traction power systems; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108895
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Resilience in rail networks is influenced by complexity of their interdependencies. Risk to electrical systems result in widescale effects in interdependent networks. Resilience can be assessed using metrics of train and passenger delay minutes. Abstract: Rail networks entail multiple interdependencies which can initiate or propagate network failures with serious consequences for the movement of trains and passengers. In this study, we present a rail infrastructure system-of-systems model which can be used to simulate disruptions to the network's operations and propose a performance metric based on train and passenger delay minutes. We demonstrate the applicability of our model by evaluating the resilience of the southern region of Great Britain's rail network in scenarios of failure initiated in the traction power supply system. The results highlight the sensitivity of the rail network to a small number of traction assets concentrated in the London area, where we find that failure of one of the region's most critical electricity traction power grid could disrupt 75% of all trains in the southern region and 25% of all trains nationally. Our analysis demonstrates how the resilience of the rail network is influenced by the speed of backup restoration following a failure event as well as the network's vulnerability to failure at peak travel times with a delay during peak travel resulting in about 63% increase in passenger delay minutes compared with off-peak travel times for the same duration.