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Simulation-Based Analyses for Critical Infrastructure Protection: Identifying Risks by Using Data Farming

In: Operations Research Proceedings 2015

Author

Listed:
  • Silja Meyer-Nieberg

    (Universität der Bundeswehr München)

  • Martin Zsifkovits

    (Universität der Bundeswehr München)

  • Dominik Hauschild

    (Universität der Bundeswehr München)

  • Stefan Luther

    (Universität der Bundeswehr München)

Abstract

Critical infrastructure protection represents one of the main challenges for decision makers today. This paper focuses on rail-based public transport and on the interaction of the station layout with passenger flows. Recurring patterns and accumulation points with high passenger densities are of great importance for an analysis since they represent e.g. critical areas for surveillance and tracking and further security implementations. An agent-based model is developed for crowd behavior in railway stations. For the analysis, we apply the methodology of data farming, an iterative, data-driven analysis process similar to the design of simulation experiments. It uses experimental designs to scan the parameter space of the model and analyses the data of the simulation runs with methods stemming from statistics and data mining. With its help, critical parameter constellations can be identified and investigated in detail.

Suggested Citation

  • Silja Meyer-Nieberg & Martin Zsifkovits & Dominik Hauschild & Stefan Luther, 2017. "Simulation-Based Analyses for Critical Infrastructure Protection: Identifying Risks by Using Data Farming," Operations Research Proceedings, in: Karl Franz Dörner & Ivana Ljubic & Georg Pflug & Gernot Tragler (ed.), Operations Research Proceedings 2015, pages 349-354, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-42902-1_47
    DOI: 10.1007/978-3-319-42902-1_47
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