This project aims to replicate the disaster in order to manage crowds in a virtual environment. Once the simulated crowd was sufficiently similar to the real crowd, empirical studies were executed to verify the simulation results. A state of the art Virtual Reality(VR) device is used to generate an immersive experience of critical parts of that event. The observation of participants’ emotional arousals and stress levels would help decision makers to better understand crowd management strategies. In the end we verified the hypothesis that better crowd management strategy can help to reduce the stress level from a first person perspective simulation. Using a simulation tool based on the Social Force Model and Unity game engine, we provided a sophisticated framework to examine crowd management strategies.

This project studies the use Virtual Reality to help scientist to research on indoor scenarios that are hard to replicate during physical experiment, such as fire evacuation. We use the virtual model of the Cooper Hewitt Museum as a case study and there are a variety of successful projects related to evidence based design (e.g., crowding/movement efficiency, design/sign placement) having been generated in the Chair of Cognitive Science. Currently, this specific project investigates the advantages of adaptive over non-adaptive signs during emergency fire evacuation in the museum. This project uses modern gaming technology to simulate a fire scenario and evaluate the benefits of dynamic guidance systems. This project also uses state of the art physiological monitoring in order to capture the participants level of stress while evacuating the building (under different conditions).

Virtual reality techniques are increasingly being employed to investigate human behavior and cognition in complex environments. A relatively new field is the application of virtual reality techniques to experimental investigations of locomotion and spatial decision-making with large groups of navigators as well as dense crowds. A primary aim of this approach is to understand the cognitive and behavioral processes leading to congestion and other group-level dynamics of emergency situations such as panic during an evacuation. This project investigates social wayfinding using the Decision Sciences Laboratory (DeSciL) at ETH and Amazon Mechanical Turk. This research will enable us to determine the impact of crowd characteristics and social cues on individuals’ wayfinding decisions.

In spatial cognitive science, researchers have been using the term cognitive maps to refer to the mental representation of large-scale environments and the location of an individual within those environments. This concept has been widely used without a formal definition. The aim of the project is to provide a computational framework for cognitive maps that distinguishes between different aspects of cognitive maps (i.e., reference frame, scale, metric, level of abstraction) without having to complicate the experimental design. This framework will consist of computational models that are relatively easy to evaluate quantitatively. The proposed model will be built on a existed one from general cognition literature (e.g., Gen- eralized Context Model) and verified with a training procedure from real experimental data using machine learning algorithms.