Pedestrian fluxes in confined geometric networks: entropic measures and robustness of accessibility in a university campus

TL;DR

This study analyzes pedestrian movements in a university campus network using Wi-Fi data, measuring entropy and robustness to assess walkability and identify critical areas for maintenance.

Contribution

It introduces a novel approach combining Wi-Fi data with entropic measures to evaluate pedestrian flow and network robustness in confined urban environments.

Findings

Wi-Fi data effectively captures pedestrian movement patterns.

Network entropy indicates areas of high and low accessibility.

Robustness analysis reveals critical pathways for maintaining walkability.

Abstract

When discussing urban life, pedestrian accessibility to all main services is crucial for fostering social interactions, promoting healthy lifestyles, and reducing pollution. This is especially relevant in coherent urban agglomerations like university campuses, which feature a high concentration of streets and social facilities. Using Wi-Fi data, we study pedestrian movements within a confined geometric network representing the pathways on a university campus. We estimate the level of crowding in each arc of the network and identify pedestrian flows along all possible paths, measuring the entropy and robustness of the network. In particular, we calculate the information gain achieved through the use of Wi-Fi data and we assess how pedestrian traffic redistributes within the network after the removal of individual arcs. Our results can be used to facilitate the investigation of the current state of walkability across the university campus while also testing a set of methods for analyzing urban complex networks, potentially allowing us to pinpoint areas in urgent need of road maintenance and enhancement.