Classification of Pedestrian Crowds by Dimensionless Numbers

Jakob Cordes, Alexandre Nicolas, Andreas Schadschneider
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Abstract

Presently, classifications of pedestrian crowds primarily rely on density. This fails to encompass the diverse behaviours and risk profiles observed. We introduce two dimensionless numbers, the Intrusion number In, based on the desire to maintain one’s personal space, and the Avoidance number Av, based on the anticipation of collisions. These two numbers delineate different flow regimes, as we intuitively expect and as we empirically demonstrate using an extensive dataset. Similarly to Fluid Mechanics, where dimensionless numbers guide the choice between different approximations, the dynamics of crowds can be approached in each regime by perturbative expansions, which yield pedestrian models applicable in the corresponding regime (and only there).
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用无量纲数对行人群进行分类
目前,对行人人群的分类主要依靠密度。这无法涵盖所观察到的各种行为和风险特征。我们引入了两个无量纲数字,一个是基于保持个人空间愿望的 "入侵数 In",另一个是基于碰撞预期的 "规避数 Av"。这两个数字划分了不同的流动状态,正如我们的直觉所预期的那样,而且我们使用大量数据集进行了经验证明。与流体力学中指导在不同近似值之间进行选择的无量纲数类似,人群动力学也可以通过微扰展开在每种状态下进行研究,从而产生适用于相应状态(且仅适用于相应状态)的行人模型。
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23 weeks
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