Predicting Crowd Egress and Environment Relationships to Support Building Design Optimization

Abstract

Evaluating and optimizing the design of built and yet-to-be-built environments, with respect to human occupancy and behavior is both greatly beneficial and challenging. Crowd simulation can provide the computational means to analyze a design through the movement of virtual occupants (agents). A range of analytic information (metrics) can be computed from the simulated movement of the agents that offer insights on the design. Crowd simulation and the related analysis can be part of interactive or offline design optimization pipelines. Unfortunately, large scale crowd simulations are prohibitively expensive, especially when used within iterative design and optimization loops, where hundreds of simulations often need to be computed at interactive rates. We propose a machine learning framework that aims to solve this problem by learning the relationship between a building design and the evaluation metrics extracted from expensive simulations. We train an offline regression neural network using a synthetic training set that we generate for this purpose. Once the network is trained it can evaluate new designs efficiently, and approximate the corresponding analytic information with high accuracy. The proposed framework can also be used to find an optimized layout. We demonstrate the effectiveness of the framework on a variety of real world case studies.