This thesis introduces new methods for modelling human wayfinding behavior in
the context of microscopic pedestrian simulation models. Using a sparse navigation graph, which is automatically derived by a scenarios’ geometry, enables a combination between macroscopic network flow models and microscopic models to derive more realistic evacuation times.
The incorporation of cognitive processes into algorithms, which are defined on the graph, leads to the realistic modelling of human wayfinding behavior.
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This thesis introduces new methods for modelling human wayfinding behavior in
the context of microscopic pedestrian simulation models. Using a sparse navigation graph, which is automatically derived by a scenarios’ geometry, enables a combination between macroscopic network flow models and microscopic models to derive more realistic evacuation times.
The incorporation of cognitive processes into algorithms, which are defined on the graph, leads to the realistic modelling of human wayfinding...
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