Collective behavior of human crowds

无论是走在繁忙的人行道上，还是走在拥挤的商场里，我们都会毫不费力地与其他行人协调行动。有时我们在人群中穿行，躲避邻居；有时我们融合成一个连贯的“群”，很像一群鸟或一群鱼，可能会自发地形成相反方向的车道，类似于蚂蚁队伍。这些基本流量模式从何而来？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人，但实际机制尚不清楚。通过研究支配邻居之间互动的知觉-运动“规则”，调查者的目标是确定人群行为是否可以通过局部互动来解释。由此得到的模型将能够逼真地模拟行人交通流量。它潜在的更广泛的影响是建筑设计、疏散规划、计算机动画以及为盲人和低视力用户开发辅助技术。在从物理和计算机科学到动物行为和城市规划等领域，有许多集体蜂群行为的模型，但它们建立在很少的实验数据基础上。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--支配行人互动的知觉-运动规则、力或控制法则。这个项目的目标是开发一个认知基础的行人模型，并测试从这些局部互动中产生全球人群行为的假设。一项创新的研究计划结合了(A)局部到全局的方法，其中视觉耦合在与虚拟人群的实验中被绘制出来，并用于在多智能体模拟中预测人群行为，以及(B)全局到局部的方法，其中对真实人群的实验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”水平之间的关系。

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.