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.

翻译：传统行人模拟器是建筑设计过程中不可或缺的工具，它们使项目工程师能够预防过度拥挤情况并规划疏散逃生路线。然而，模拟运行时间及生成模拟结果所需的多个繁琐步骤，成为建筑设计过程中的潜在瓶颈。数据驱动方法已被证明能够在速度上超越传统方法，同时在许多学科领域提供相似甚至更优的结果。本文提出了一种基于视觉变换器的深度学习方法，用于从给定建筑平面图预测随时间变化的密度热力图及总疏散时间。具体而言，鉴于公开数据集的有限性，我们实现了一个包含传统模拟器的参数化数据生成流水线，从而构建了用于训练架构的大型合成数据集。此外，我们将该模型无缝集成至BIM建模工具中，实现模拟结果的即时自动生成。