Deep learning approach for predicting pedestrian dynamics for transportation hubs in early design phases

A seamless integration of model analysis and simulations into the design process is a key for supporting the different decisions, including deciding upon the position, dimensions, and materiality of building elements. Such design options are explored from the early design phases, where a decision is taken based on their performance. A crucial analysis that is necessary for the different types of buildings, especially transportation hubs, is pedestrian flow dynamics, as it evaluates the occupants' comfort and ability to evacuating the building in case of emergency. Currently, analysing pedestrians' flow is decoupled from the BIM-authoring tools, requires multiple manual steps, and is time consuming. Hence, this paper proposes a framework that leverages the latest advancements of Deep Learning (DL) for replacing pedestrian dynamics simulations by an DL model providing intermediate feedback. In more detail, a representation of the building model, including simulation parameters, is proposed as input and a Convolutional Neural Network (CNN) architecture is developed and trained to predict pedestrians' flow density heatmaps and tracing maps.