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Kpotissan Adjetey-Bahun, Babiga Birregah, Eric Châtelet, Jean-Luc Planchet, & Edgar Laurens-Fonseca. (2014). A simulation-based approach to quantifying resilience indicators in a mass transportation system. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 75–79). University Park, PA: The Pennsylvania State University.
Abstract: A simulation-based model used to measure resilience indicators of the railway transportation system is presented. This model is tested through a perturbation scenario: the inoperability of a track which links two stations in the system. The performance of the system is modelled through two indicators: (a) the number of passengers that reach their destination and (b) the total delay of passengers after a serious perturbation. The number of passengers within a given station at a given time is considered as early warning in the model. Furthermore, a crisis management plan has been simulated for this perturbation scenario in order to help the system to recover quickly from this perturbation. This crisis management plan emphasizes the role and the importance of the proposed indicators when managing crises.
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Diana Contreras, Thomas Blaschke, Stefan Kienberger, & Peter Zeil. (2011). Spatial vulnerability indicators: Measuring recovery processes after earthquakes. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In order to analyze and evaluate any post-disaster phases it is necessary to address the pre-existent vulnerability conditions. The methodology consists of four steps: the first step comprises of a review of vulnerability and recovery indicators; the second step is to identify indicators based on spatial variables; the third step is to find the common variables among the subsets of spatial variables from vulnerability and recovery indicators; and the fourth step more pragmatic, is an investigation of the availability of data. The initial results are the set of vulnerability and recovery indicators. Reducing the set of indicators to the indicators represented in a spatial context and the indicators with common features of vulnerability and recovery indices bears the risk to ignore some important single indicators; nevertheless, the added value of the on-going research is to show the advantages of using indicators based on spatial variables.
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Michael Hiete, & Mirjam Merz. (2009). An indicator framework to assess the vulnerability of industrial sectors against indirect disaster losses. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Natural and man-made hazards may affect industrial production sites by both direct losses (due to physical damage to assets and buildings) and indirect losses (production losses). Indirect losses, e.g. from production downtimes, can exceed direct losses multiple times. Thus, the vulnerability of industrial sectors to indirect losses is an important component of risk and its determination is an important part within risk analysis. In this paper a conceptual indicator framework is presented which allows to assess the indirect vulnerability of industrial sectors to different types of disasters in a quantitative manner. The results are useful for information sharing and decision making in crisis management and emergency planning (mitigation measures, business continuity planning), since the developed indicator system helps to take the complex phenomenon of industrial vulnerability and the underlying interdependencies into account. Besides the identification and conceptual motivation of the indicators, methodical aspects such as standardization, weighting and aggregation are addressed.
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