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Assessing Protection Strategies for Urban Rail Transit Systems: A Case-Study on the Central London Underground

Esposito Amideo, Annunziata, Starita, Stefano, Scaparra, Maria Paola (2019) Assessing Protection Strategies for Urban Rail Transit Systems: A Case-Study on the Central London Underground. Sustainability, 11 (22). Article Number 6322. ISSN 2071-1050. (doi:10.3390/su11226322) (KAR id:78587)


Urban rail transit systems are highly prone to disruptions of various nature (e.g., accidental, environmental, man-made). Railway networks are deemed as critical infrastructures given that a service interruption can prompt adverse consequences on entire communities and lead to potential far-reaching effects. Hence, the identification of optimal strategies to mitigate the negative impact of disruptive events is paramount to increase railway systems’ resilience. In this paper, we investigate several protection strategies deriving from the application of either single asset vulnerability metrics or systemic optimization models. The contribution of this paper is threefold. Firstly, a single asset metric combining connectivity, path length and flow is defined, namely the Weighted Node Importance Evaluation Index (WI). Secondly, a novel bi-level multi-criteria optimisation model, called the Railway Fortification Problem (RFP), is introduced. RFP identifies protection strategies based on stations connectivity, path length, or travel demand, considered as either individual or combined objectives. Finally, two different protection strategy approaches are applied to a Central London Underground case study: a sequential approach based on single-asset metrics and an integrated approach based on RFP. Results indicate that the integrated approach outperforms the sequential approach and identifies more robust protection plans with respect to different vulnerability criteria. View Full-Text

Item Type: Article
DOI/Identification number: 10.3390/su11226322
Uncontrolled keywords: critical infrastructures (CI); railway systems; protection to disruptions; optimisation
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Paola Scaparra
Date Deposited: 12 Nov 2019 11:50 UTC
Last Modified: 10 Dec 2022 06:08 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Scaparra, Maria Paola.

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