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Measuring dynamics in evacuation behaviour with deep learning
- Bounded rationality is one crucial component in human behaviours. It plays a key role in the typical collective behaviour of evacuation, in which heterogeneous information can lead to deviations from optimal choices. In this study, we propose a framework of deep learning to extract a key dynamical parameter that drives crowd evacuation behaviour in a cellular automaton (CA) model. On simulation data sets of a replica dynamic CA model, trained deep convolution neural networks (CNNs) can accurately predict dynamics from multiple frames of images. The dynamical parameter could be regarded as a factor describing the optimality of path-choosing decisions in evacuation behaviour. In addition, it should be noted that the performance of this method is robust to incomplete images, in which the information loss caused by cutting images does not hinder the feasibility of the method. Moreover, this framework provides us with a platform to quantitatively measure the optimal strategy in evacuation, and this approach can be extended to other well-designed crowd behaviour experiments.
Author: | Huaidian Hou, Lingxiao WangORCiD |
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URN: | urn:nbn:de:hebis:30:3-817678 |
DOI: | https://doi.org/10.3390/e24020198 |
ISSN: | 1099-4300 |
Parent Title (English): | Entropy |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2022/01/27 |
Date of first Publication: | 2022/01/27 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2024/01/22 |
Tag: | crowd behaviour; deep learning; evacuation |
Volume: | 24 |
Issue: | 2, art 198 |
Article Number: | 198 |
Page Number: | 11 |
First Page: | 1 |
Last Page: | 11 |
Note: | This research was supported by the AI grant at FIAS of SAMSON AG, Frankfurt (L.W.). |
HeBIS-PPN: | 519157532 |
Institutes: | Physik |
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS) | |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik | |
Sammlungen: | Universitätspublikationen |
Licence (German): | ![]() |