Resilience Assessment of City-Scale Transportation Networks Using Monte Carlo Simulation

To improve the resilience of critical infrastructure systems, their intrinsic properties need to be understood and their resilience state needs be identified. In the literature, several methods to evaluate networks’ reliability and resilience can be found. However, the applicability of these methods is usually restricted to small-size net-works. In this paper, the transportation network of a large-scale virtual city is considered as a case study. A random removal of the roads is applied simulating the network’s failure. The network reliability is then calculated using the Destruction Spectrum (D-spectrum) method and a Monte Carlo approach has been developed to generate failure permutations that are necessary for the evaluation of the D-spectrum se. In addition, the Birnbaum Importance Measure (BIM) has been adopted in this study to determine the importance of the net-work’s components. The methodology adopted in this study can be also extended to all network-based systems. The paper also introduces resilience indicators as a soft tool to predict the performance and serviceability of transportation networks.