Crowded transport within networked representations of complex geometries

Abstract Transport in crowded, complex environments occurs across many spatial scales. Geometric restrictions can hinder the motion of individuals and, combined with crowding, have drastic effects on global transport phenomena. However, general, interplay between crowding and geometry real-life is poorly understood. Existing analytical methodologies are not always readily extendable to heterogeneous these situations, predictions crowded behaviour rely computationally intensive mesh-based approaches. Here, we take a different approach based networked representations order provide an efficient framework explore interactions environments’ crowding. We demonstrate how this be used extract detailed information both at level individual as well whole population, identify topological features that enable accurate prediction phenomena, insights into design optimal environments.