We consider the problem of categorizing and describing 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, loosely based on the statechart formalism from computer science. This uses relatively "agnostic" labels, which 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 as "threads" of states, and allows for the dynamic handling of an arbitrary number of "sub-crowds".

翻译：我们研究了随时间变化的人群动态特性与行为的分类及描述问题。以往研究多集中于相对静态的"类型学"方法，但这种方法未能考虑人群（尤其可能快速）变化的事实。此外，对人群行为的标签往往带有主观性和/或价值判断。本文提出一种替代方案，该方案大致基于计算机科学中的状态图形式化方法。其采用相对"不可知"的标签——即我们不预设个体的行为，而是提供个体可能行为的环境背景。该方法将人群的时间序列演化自然地描述为状态的"线程"，并支持对任意数量"子人群"的动态处理。