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.
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The support from the German Federal Ministry of Education and Research (BMBF) (project RiKoV, Grant No.13N12304) is gratefully acknowledgement.
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Meyer-Nieberg, S., Zsifkovits, M., Hauschild, D., Luther, S. (2017). Simulation-Based Analyses for Critical Infrastructure Protection: Identifying Risks by Using Data Farming. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_47
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DOI: https://doi.org/10.1007/978-3-319-42902-1_47
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