A dynamic state-based model of crowds

Martyn Amos*, Paul Gainer, Steve Gwynne, Anne Templeton

*Corresponding author for this work

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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 “typology”-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 “agnostic” 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 “sub-crowds”. 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.
Original languageEnglish
Article number106522
Number of pages9
JournalSafety Science
Early online date6 Apr 2024
Publication statusE-pub ahead of print - 6 Apr 2024

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