Crowded transport within networked representations of complex geometries

TL;DR

This paper introduces a network-based framework to analyze how crowding and complex geometries influence transport phenomena, offering efficient predictions and insights for environment design.

Contribution

It presents a novel networked approach to study crowded transport in complex geometries, overcoming limitations of traditional mesh-based methods.

Findings

Network representations effectively model crowded transport.

Topological features predict transport efficiency.

Framework aids in designing optimal environments.

Abstract

Transport in crowded, complex environments occurs across many spatial scales. Geometric restrictions can hinder the motion of individuals and, combined with crowding between individuals, 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: in these situations predictions of crowded transport behaviour within heterogeneous environments rely on computationally intensive mesh-based approaches. Here, we take a different approach by employing networked representations of complex environments to provide an efficient framework within which the interactions between networked geometry and crowding can be explored. We demonstrate how the framework can be used to: extract detailed information at the level of the whole population or an individual within it; identify the topological features of environments that enable accurate prediction of transport phenomena; and, provide insights into the design of optimal environments.