Critical area prediction in train stations using interactive object detection with Detection Transformer

With increasing global passenger numbers, train stations face challenges in accommodating higher volumes while ensuring safety. Urbanization exacerbates this issue in densely populated, built-up areas with limited space. This poses increased safety risks due to potential overcrowding, especially during emergency evacuations. Therefore, integrating pedestrian simulators into early planning stages is crucial, yet time-consuming and manual, hindering thorough exploration of building design variants. In this article, we propose an interactive solution using a Detection Transformer to immediately assess the impact of design decisions on pedestrian flow and safety. Specifically, we present a digital train station model utilizing parametric Building Information Modeling (BIM) to generate many hundreds of floorplan variants to train our neural network. We then connect the trained network to a BIM authoring software, which allows for immediate evaluation of design changes. To address the diverse design parameter space, we present distinct customization options for the network to integrate supplementary inputs, merging simulator and floorplan data for accurate predictions. Notably, our approach conforms to official train station design guidelines and congestion standards, demonstrating its applicability in practice and ensuring compliance with safety regulations. To showcase the effectiveness and versatility of our methodology, we apply it to a real-world use case.