Golas Abhinav, Narain Rahul, Lin Ming C
University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
University of California, Berkeley, Berkeley, California 94720, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Oct;90(4):042816. doi: 10.1103/PhysRevE.90.042816. Epub 2014 Oct 28.
With the growth in world population, the density of crowds in public places has been increasing steadily, leading to a higher incidence of crowd disasters at high densities. Recent research suggests that emergent chaotic behavior at high densities-known collectively as crowd turbulence-is to blame. Thus, a deeper understanding of crowd turbulence is needed to facilitate efforts to prevent and plan for chaotic conditions in high-density crowds. However, it has been noted that existing algorithms modeling collision avoidance cannot faithfully simulate crowd turbulence. We hypothesize that simulation of crowd turbulence requires modeling of both collision avoidance and frictional forces arising from pedestrian interactions. Accordingly, we propose a model for turbulent crowd simulation, which incorporates a model for interpersonal stress and acceleration constraints similar to real-world pedestrians. Our simulated results demonstrate a close correspondence with observed metrics for crowd turbulence as measured in known crowd disasters.
随着世界人口的增长，公共场所人群的密度一直在稳步增加，导致高密度人群灾难的发生率更高。最近的研究表明，高密度下出现的集体混乱行为——统称为人群湍流——是罪魁祸首。因此，需要更深入地了解人群湍流，以促进预防和规划高密度人群混乱状况的工作。然而，已经注意到现有的模拟避撞算法无法如实地模拟人群湍流。我们假设，模拟人群湍流需要对避撞和行人相互作用产生的摩擦力进行建模。因此，我们提出了一种用于湍流人群模拟的模型，该模型纳入了一个类似于现实世界行人的人际压力和加速度约束模型。我们的模拟结果表明，与已知人群灾难中测量的人群湍流观测指标密切相关。
