Towards predicting Pedestrian Evacuation Time and Density from
  Floorplans using a Vision Transformer
Patrick Berggold Stavros Nousias Rohit K. Dubey André Borrmann
Conventional pedestrian simulators are inevitable tools in the design process
of a building, as they enable project engineers to prevent overcrowding
situations and plan escape routes for evacuation. However, simulation runtime
and the multiple cumbersome steps in generating simulation results are
potential bottlenecks during the building design process. Data-driven
approaches have demonstrated their capability to outperform conventional
methods in speed while delivering similar or even better results across many
disciplines. In this work, we present a deep learning-based approach based on a
Vision Transformer to predict density heatmaps over time and total evacuation
time from a given floorplan. Specifically, due to limited availability of
public datasets, we implement a parametric data generation pipeline including a
conventional simulator. This enables us to build a large synthetic dataset that
we use to train our architecture. Furthermore, we seamlessly integrate our
model into a BIM-authoring tool to generate simulation results instantly and
automatically.