Due to the continuous increment in complexity of the socio-technical systems, decision makers call for new methods which are able to support timely as well as accurate decisionmaking related to resilience management. The current methods tend to be polarized on: efficiency-thoroughness forcing decision makers in making decisions on the base of resource availability instead of the problem to be solved. This paper presents a new fast-forward, cost-effective, and thorough enough framework to quantify resilience of a complex socio-technical system. The approach extends the functional resonance analysis method (FRAM) with a numerical method for the quantification of the analysis (Q-FRAM). In particular, it has been extended and operationalized the qualitative concepts of functional variability and dumping capacities into a method in which key performance indicators are derived from the model and aggregated into four indicators representing the FRAM resilience cornerstones (anticipate, respond, monitor, learn) through a bottom-up hierarchical approach. Finally, the four indicators are composed in a unique system resilience index that expresses the total variability present in the system at instant t. A numerical example of the use of the framework is provided together with a validation based on a comparison of the proposed approach with the current landscape.

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Critical transport infrastructures such as interchanges, long tunnels and bridges represent the most vulnerable environments within a transportation network; they are characterized by limited access/egress points and high volumes of users in close confines. A number of factors must be addressed to ensure maximum safety of both travellers and emergency service personnel; these include emergency preparedness, timely support for the decision-making process, and planning of optimal interventions in emergency management situations. This paper discusses the integration of wireless sensor networks (WSNs) and virtual reality (VR) to support the self-evacuation of travellers and operational procedures of rescue personnel within these environments, focusing on two key aspects of emergency operations: (i) collecting real-time data and (ii) improving the timeliness of first responders through efficient provision of the collected data. Testimonials, technical results and recommendations collated from two pilot installations realised within the EU-funded SAVE ME project demonstrate the qualitative and quantitative impact of such an approach on emergency situation management.