Collective behavior of human crowds

无论是走在忙碌的人行道上还是拥挤的商场里，我们都毫不费力地与其他行人协调行动。 有时我们在人群中穿梭，避开邻居;有时我们会融入一个连贯的“群体”，就像一群鸟或一群鱼，可能会自发地形成相反方向的车道，类似于蚂蚁列。 这些基本的交通模式从何而来？ 人们普遍认为，人类和动物的集体行为来自邻居之间的局部互动，而不是来自中央计划或领导者，但实际机制尚不清楚。 通过研究支配邻居之间相互作用的感知运动“规则”，研究人员旨在确定群体行为是否可以用局部相互作用来解释。 由此产生的模型将能够逼真地模拟行人交通流。其潜在的更广泛的影响是建筑设计，疏散规划，计算机动画，以及盲人和低视力用户辅助技术的发展。从物理学、计算机科学到动物行为学和城市规划等领域都有许多关于群体集体行为的模型，但它们都是基于很少的实验数据。 现有理论的主要弱点是缺乏关于邻居之间视觉耦合的知识-感知运动规则，力量或控制行人互动的控制法则。这个项目的目标是开发一个认知接地行人模型和测试的假设，全球人群行为出现这些地方的相互作用。 一个创新的研究计划结合了（a）局部到全局的方法，其中的视觉耦合映射出虚拟人群的实验，并用于预测多智能体模拟人群的行为，和（B）全球到局部的方法，其中的实验对真实的人群进行分析，并用于测试模型。其目的是解释行人和人群的动态，并阐明集体行为的“微观”和“宏观”层面之间的关系。

Whether walking down a busy sidewalk or a crowded mall, we effortlessly coordinate our movements with other pedestrians. Sometimes we weave through the crowd, dodging our neighbors; at other times we merge into a coherent "swarm," much like a flock of birds or a school of fish, and may spontaneously form lanes in opposite directions, similar to columns of ants. Where do these basic traffic patterns come from? It is generally believed that collective behavior in humans and animals emerges from local interactions between neighbors, rather than from a central plan or leader, but the actual mechanisms are unclear. By studying the perceptual-motor "rules" that govern interactions between neighbors, the investigator aims to determine whether crowd behavior can be explained by local interactions. The resulting model will enable realistic simulation of pedestrian traffic flow. Its potential broader impacts are to architectural design, evacuation planning, computer animation, and the development of assistive technology for blind and low-vision users. There are many models of collective swarm behavior in fields ranging from physics and computer science to animal behavior and urban planning, yet they are based on little experimental data. The key weakness of existing theories is a dearth of knowledge about the visual coupling between neighbors -- the perceptual-motor rules, forces, or control laws that govern pedestrian interactions. The goal of this project is to develop a cognitively grounded pedestrian model and test the hypothesis that global crowd behavior emerges from these local interactions. An innovative research program combines (a) a local-to-global approach, in which the visual coupling is mapped out in experiments with virtual crowds, and used to predict crowd behavior in multi-agent simulations, and (b) a global-to-local approach, in which experiments on real crowds are analyzed and used to test the model. The aim is to account for pedestrian and crowd dynamics and elucidate the relation between "micro" and "macro" levels of collective behavior.