Citation

Amos M, Gainer P, Gwynne S, Templeton A. Safety Sci. 2024; 175: e106522.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.ssci.2024.106522

PMID

unavailable

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 "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.

Language: en

Keywords

Crowd; Event analysis; Model; Simulation; Statechart; Typology