Network-based approach to improve the robustness and resilience analysis of urban railway networks

Abstract

Urban railway networks (URNs), as the backbone of public transport networks (PTNs) in mega-cities, have provided efficient, convenient, high-efficiency, reliable, and affordable services to people. Since a myriad of mega-cities has entered the era of mass transit railway, there have emerged many unprecedented challenges for URNs, such as frequent extreme weather events, uncontrollable man-made attacks, and ageing problems. Therefore, a pressing need is required to quantify and predict the adaptabilities of URNs to diverse disruptions. In response, resilience has been adopted as a cutting-edge solution to manage resilient urban rail transit systems and improve the system's performance against disruptive events. Despite in-depth exploration in previous studies, it is not easy to implement the complex concept and principles of resilience in an infrastructure system in practice. The management and operation of a resilient URN require sufficient domain knowledge about resilience assessment and available data with high quality, data mining the dataset, and information integration. As the layout of URNs has transformed into the era of complex networks, complex network theory, as a multidisciplinary research method, has emerged as a constructive tool to identify inherent vulnerabilities, quantify network robustness, and select effective restoration strategies.

However, current studies are insufficient to provide an integrated network-based approach to different perspectives on URN resilience. Therefore, this research aims to develop an aggregated network-based approach to improve the robustness and resilience analysis of URNs, synergizing topology- and flow-based domain knowledge. The research was carried out by employing a multi-method research strategy, including quantitative and qualitative methods.

First, comprehensive literature reviews were conducted to streamline the prior and current knowledge covering the aspects of urban railway networks, resilience, robustness, complex network theory, cascading failure model, multi-criteria decision-making (MCDM) method, and time-dependent network model. Findings from the reviews of previous studies revealed the inadequacy of an overarching and synergistic approach for the robustness of resilience analysis of URNs. Second, cutting-edge research toolkits were employed to identify the inherent vulnerabilities, unveil the urban mobility pattern, analyse the dynamic robustness, and assess resilience incorporating both topology- and flow-based methods. Third, empirical cases in Hong Kong were utilised to demonstrate the applicability and validity of this synergistic approach. Last, an in-depth and comparative discussion was carried out to holistically analyse the results derived from diverse perspectives and, therefore, confirm the approach's feasibility.

This research initially provides an aggregated approach to multifaceted robustness and resilience evaluation and analyses of URNs in mega-cities with the demonstration on the Mass Rail Transit (MTR) in Hong Kong. The proposed approach and corresponding findings have made contributions to the knowledge domain of managing networked infrastructure systems and drawn light upon the related decision-making process from theoretical, methodological, and practical perspectives.