Towards predicting Pedestrian Evacuation Time and Density from Floorplans using a Vision Transformer

Patrick Berggold, Stavros Nousias, Rohit K. Dubey, André Borrmann

Research output: Contribution to conferencePaperpeer-review

Abstract

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.

Original languageEnglish
StatePublished - 2023
Event30th International Conference on Intelligent Computing in Engineering 2023, EG-ICE 2023 - London, United Kingdom
Duration: 4 Jul 20237 Jul 2023

Conference

Conference30th International Conference on Intelligent Computing in Engineering 2023, EG-ICE 2023
Country/TerritoryUnited Kingdom
CityLondon
Period4/07/237/07/23

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