@article{ref1,
title="A dynamic state-based model of crowds",
journal="Safety science",
year="2024",
author="Amos, Martyn and Gainer, Paul and Gwynne, Steve and Templeton, Anne",
volume="175",
number="",
pages="e106522-e106522",
abstract="We consider the problem of categorising, describing and generating the dynamic properties and behaviours of crowds over time. Previous work has tended to focus on a relatively static &quot;typology&quot;-based approach, which does not account for the fact that crowds can change, often quite rapidly. Moreover, the labels attached to crowd behaviours are often subjective and/or value-laden. Here, we present an alternative approach which uses relatively &quot;agnostic&quot; labels. This means that we do not prescribe the behaviour of an individual, but provide a context within which an individual might behave. This naturally describes the time-series evolution of a crowd, and allows for the dynamic handling of an arbitrary number of &quot;sub-crowds&quot;. Apart from its descriptive power (capturing, in a standardised manner, descriptions of known events), our model may also be used generatively to produce plausible patterns of crowd dynamics and as a component of machine learning-based approaches to investigating behaviour and interventions.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2024.106522",
url="http://dx.doi.org/10.1016/j.ssci.2024.106522"
}