Mayo, Hugo;
Shipman, Alastair;
Giunchi, Daniele;
Bovo, Riccardo;
Steed, Anthony;
Heinis, Thomas;
(2021)
VR Toolkit for Identifying Group Characteristics.
Collective Dynamics
, 6
pp. 1-17.
10.17815/CD.2021.119.
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Abstract
Visualising crowds is a key pedestrian dynamics topic, with significant research efforts aiming to improve the current state-of-the-art. Sophisticated visualisation methods are a standard for modern commercial models, and can improve crowd management techniques and sociological theory development. These models often define standard metrics, including density and speed. However, modern visualisation techniques typically use desktop screens. This can limit the capability of a user to investigate and identify key features, especially in real time scenarios such as control centres. Virtual reality (VR) provides the opportunity to represent scenarios in a fully immersive environment, granting the user the ability to quickly assess situations. Furthermore, these visualisations are often limited to the simulation model that has generated the dataset, rather than being source-agnostic. In this paper we implement an immersive, interactive toolkit for crowd behaviour analysis. This toolkit was built specifically for use within VR environments and was developed in conjunction with commercial users and researchers. It allows the user to identify locations of interest, as well as individual agents, showing characteristics such as group density, individual (Voronoi) density and speed. Furthermore, it was used as a data-extraction tool, building individual fundamental diagrams for all scenario agents, and predicting group status as a function of local agent geometry. Finally, this paper presents an evaluation of the toolkit made by crowd behaviour experts.
| Type: | Article |
|---|---|
| Title: | VR Toolkit for Identifying Group Characteristics |
| Event: | Pedestrian and Evacuation Dynamics 2021 |
| Dates: | 29 Nov 2021 - 30 Nov 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.17815/CD.2021.119 |
| Publisher version: | https://doi.org/10.17815/CD.2021.119 |
| Language: | English |
| Additional information: | Copyright © 2021 Hugo Mayo, Alastair Shipman, Daniele Giunchi, Riccardo Bovo, Anthony Steed, Thomas Heinis. This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10149127 |
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