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000189708 020__ $$a978-3-95806-032-6
000189708 037__ $$aFZJ-2015-02743
000189708 041__ $$aEnglish
000189708 1001_ $$0P:(DE-Juel1)145946$$aEilhardt, Christian$$b0$$eCorresponding Author$$gmale$$ufzj
000189708 245__ $$aComputer simulation of pedestrian dynamics at high densities$$f2014-10-17
000189708 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2015
000189708 300__ $$aviii, 142 S.
000189708 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1432125832_11929
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000189708 4900_ $$aSchriften des Forschungszentrums Jülich. IAS Series$$v29
000189708 502__ $$aUniversität Köln, Diss., 2014$$bDr.$$cUniversität Köln$$d2014
000189708 520__ $$aThe increasing importance and magnitude of large-scale events in our society calls for continuous research in the field of pedestrian dynamics. This dissertation investigates the dynamics of pedestrian motion at high densities using computer simulations of stochastic models. The first part discusses the successful application of the Floor Field Cellular Automaton (FFCA) in an evacuation assistant that performs faster than real-time evacuation simulations of up to 50,000 persons leaving a multi-purpose arena. A new interpretation of the matrix of preference improves the realism of the FFCA simulation in U-turns, for instance at the entrance to the stands. The focus of the second part is the experimentally observed feature of phase separation in pedestrian dynamics into a slow-moving and a completely jammed phase. This kind of phase separation is fundamentally different to known instances of phase separation in e.g. vehicular traffic. Different approaches to modeling the phase separation are discussed and an investigation of both established and new models of pedestrian dynamics illustrates the difficulties of finding a model able to reproduce the phenomenon. The Stochastic Headway Dependent Velocity Model is introduced and extensively analyzed, simulations of the model evolve into a phase-separated state in accordance with the experimental data. Key components of the model are its slow-tostart rule, minimum velocity, and large interaction range.
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