Collective Motion in Human Crowds: Tests of the Weighted-Averaging Model
Collective Motion in Human Crowds: Tests of the Weighted-Averaging Model
复制标题
人群中的集体运动：加权平均模型的检验
DOI：
10.1167/jov.20.11.287
复制
发表时间：
2020
影响因子：
1.8
通讯作者：
Warren, William H.
中科院分区：
文献类型：
--
作者：
Willcoxon, Meghan;Warren, William H.
行人之间的局部互动会引起人群中的集体运动吗？里约热内卢，Dachner & Warren （PRSB 2018）开发了一个实验驱动的模型，在这个模型中，每个人都将自己的前进方向（和速度）与邻居的前进方向（或速度）的加权平均值保持一致，并且权重随着距离的增加呈指数衰减。该模型做了两个假设：(1)行人的响应是基于小区内邻居的平均值，(2)步行速度和方向是独立控制的。我们在两个实验中验证了这些假设。在每次试验中，参与者都要与HMD上的虚拟人群“同行”，并对12个虚拟邻居的行走方向或速度进行干扰。实验1通过操纵虚拟人群中标题（或速度）的分布来测试平均假设。为了将均值与模态（4个具有相同运动的邻居）分离开来，我们改变了分布的偏度（正态，正偏态，负偏态）。正如模型预测的那样，我们发现抽穗分布之间没有显著差异（BF01= 8.46）。相比之下，有速度分布的影响(p<。001年;BF10= 42.15)：当模态邻居减慢时，最终速度更快（负偏斜）。令人惊讶的是，该模型得出了类似的结果，因为它经过了速度较慢的邻居。实验2检验了独立控制假设。我们干扰了相邻子集的航向、速度或两者兼有，并寻找串扰。当行人稍微减速转弯时，头部扰动会影响参与者的速度（p< 0.05）（Hicheur, et . 2005）。头部和速度扰动的组合也影响了参与者的头部：当邻居转弯和减速时，参与者转向更多（p< 0.05）。该模型显示出类似的反应，因为速度较慢的邻居越靠越近，从而产生更大的影响。结果证实了平均控制和独立控制的假设，并揭示了航向和速度如何在世界范围内耦合。
What local interactions between pedestrians give rise to collective motion in crowds? Rio, Dachner & Warren (PRSB 2018) developed an experiment-driven model in which each individual aligns their heading direction (and speed) with a weighted average of their neighbors’ heading (or speed), and the weight decays exponentially with distance. The model makes two assumptions:(1) a pedestrian’s response is based on the average of neighbors within the neighborhood, and (2) walking speed and heading direction are controlled independently. We tested these assumptions in two experiments. On each trial, the participant ‘walked with’a virtual crowd viewed in an HMD, and the walking direction or speed of 12 virtual neighbors was perturbed. Experiment 1 tested the averaging assumption by manipulating the distribution of headings (or speeds) in the virtual crowd. To dissociate the mean from the mode (4 neighbors with same motion), we varied the skewness of the distribution (normal, positive skew, negative skew). As the model predicts, we found no significant differences between heading distributions (BF01= 8.46). In contrast, there was an effect of speed distribution (p<. 001; BF10= 42.15): final speed was faster when modal neighbors slowed down (negative skew). Surprisingly, the model yielded similar results, as it passed slower neighbors. Experiment 2 tested the assumption of independent control. We perturbed the heading, speed, or both, of a subset of neighbors and looked for crosstalk. Heading perturbations influenced participant speed, as pedestrians slow down slightly to turn (p< 0.05)(Hicheur, et al. 2005). The combination of heading and speed perturbations also influenced participant heading: participants turned more when neighbors turned and slowed down (p< 0.05). The model showed a similar response, because slower neighbors drift closer and thus exert greater influence. The results confirm both assumptions of averaging and independent control, and reveal how heading and speed are coupled through the world.