Local interactions underlying collective motion in human crowds
Local interactions underlying collective motion in human crowds
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DOI：
10.1098/rspb.2018.0611
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发表时间：
2018-05-16
影响因子：
4.7
通讯作者：
Warren, William H.
中科院分区：
文献类型：
--
作者：
Rio, Kevin W.;Dachner, Gregory C.;Warren, William H.
人们普遍认为，群体、鱼群和人群的全球运动模式是通过个体之间的局部相互作用，通过自我组织过程而形成的。因此，解释这种集体行为的关键在于解读这些局部的相互作用。我们采取实验驱动的方法来模拟人群中的集体运动。以前，我们观察到行人将他们的速度矢量（速度和航向）与邻居的速度矢量对齐。在这里，我们调查的邻居在人群中的相互作用：哪些邻居影响行人的行为，这如何取决于邻居的位置，以及如何结合多个邻居的影响。在三个实验中，一名参与者走在一个虚拟的人群中，他们的速度和方向都被操纵了。我们发现，邻居的影响是线性组合，并随距离而减小，但不与横向位置（偏心）。我们将邻域建模为（i）一个圆形对称区域，（ii）邻居的加权平均值，（iii）一个方向性影响，以及（iv）5 m时指数衰减到零的权重。该模型再现了实验数据，并预测个人的轨迹在人类的“群”的观测数据。结果产生了第一个自下而上的集体人群运动模型。
It is commonly believed that global patterns of motion in flocks, schools and crowds emerge from local interactions between individuals, through a process of self-organization. The key to explaining such collective behaviour thus lies in deciphering these local interactions. We take an experiment-driven approach to modelling collective motion in human crowds. Previously, we observed that a pedestrian aligns their velocity vector (speed and heading direction) with that of a neighbour. Here we investigate the neighbourhood of interaction in a crowd: which neighbours influence a pedestrian's behaviour, how this depends on neighbour position, and how the influences of multiple neighbours are combined. In three experiments, a participant walked in a virtual crowd whose speed and heading were manipulated. We find that neighbour influence is linearly combined and decreases with distance, but not with lateral position (eccentricity). We model the neighbourhood as (i) a circularly symmetric region with (ii) a weighted average of neighbours, (iii) a urn-directional influence, and (iv) weights that decay exponentially to zero by 5 m. The model reproduces the experimental data and predicts individual trajectories in observational data on a human 'swarm'. The results yield the first bottom-up model of collective crowd motion.