TY  - JOUR
A1  - Hou, Huaidian
A1  - Wang, Lingxiao
T1  - Measuring dynamics in evacuation behaviour with deep learning
T2  - Entropy
N2  - 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.
KW  - deep learning
KW  - crowd behaviour
KW  - evacuation
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/81767
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-817678
SN  - 1099-4300
N1  - This research was supported by the AI grant at FIAS of SAMSON AG, Frankfurt (L.W.).
VL  - 24
IS  - 2, art 198
SP  - 1
EP  - 11
PB  - MDPI
CY  - Basel
ER  -