Abstract
AbstractTransport in crowded, complex environments occurs across many spatial scales. Geometric restrictions can hinder the motion of individuals and, combined with crowding, can have drastic effects on global transport phenomena. However, in general, the interplay between crowding and geometry in complex real-life environments is poorly understood. Existing analytical methodologies are not always readily extendable to heterogeneous environments and, in these situations, predictions of crowded transport behaviour rely on computationally intensive mesh-based approaches. Here, we take a different approach based on networked representations of complex environments in order to provide an efficient framework to explore the interactions between environments’ geometry and crowding. We demonstrate how this framework can be used to extract detailed information both at the level of the individual as well as of the whole population, identify the environments’ topological features that enable accurate prediction of transport phenomena, and provide insights into the design of optimal environments.
Funder
RCUK | Engineering and Physical Sciences Research Council
RCUK | Biotechnology and Biological Sciences Research Council
National Science Foundation
Royal Society International Exchanges Scheme Royal Society Wolfson Research Merit Award Leverhulme Research Fellowship
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy
Cited by
1 articles.
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