Analysis of the use of behavioral data from virtual reality for calibration of agent-based evacuation models.

Agent-based evacuation modeling represents an effective tool for making predictions about evacuation aspects of buildings such as evacuation times, congestions, and maximum safe building capacity. Collection of real behavioral data for calibrating agent-based evacuation models is time-consuming, costly, and completely impossible in the case of buildings in the design phase, where predictions about evacuation behavior are especially needed. In recent years evacuation experiments conducted in virtual reality (VR) have been frequently proposed in the literature as an effective tool for collecting data about human behavior.

Isovists compactness and stairs as predictors of evacuation route choice.

The building design is a crucial factor that can be actively adjusted and optimized to prevent human and property threats in emergency scenarios. Previous research suggests that specific building layouts may significantly influence human behaviour during evacuation. However, detailed empirical data about human behaviour in various types of buildings with different layouts are still missing and only marginal recommendations from this field are reflected in actual construction practice.