k-Nearest-Neighbor interaction induced self-organized pedestrian counter flow
k-Nearest-Neighbor interaction induced self-organized pedestrian counter flow
复制标题
k-最近邻交互引起的自组织行人逆流
DOI：
10.1016/j.physa.2010.01.014
复制
发表时间：
2010-05-15
影响因子：
3.3
通讯作者：
Liao, Guang-xuan
中科院分区：
文献类型：
--
作者：
Ma, Jian;Song, Wei-guo;Liao, Guang-xuan
最近的一项Field研究证实，动物群体行为是由k近邻的相互作用而不是给定度量距离内的所有邻居的相互作用所主导的。由于具有局部相互作用的系统表现出类似的自组织现象，本文建立了两个模型。即基于度量距离的模型和基于简单离散元胞自动机模型的k-最近邻（kNN）逆流模型，以基本模型为基础，研究控制行人逆流的基本交互作用。行人在长通道中移动，因此分为左行行人和右行行人。这些行人在不同的模型中以不同的形式相互作用。在基于度量距离的模型中，一个人选择的行为方向受到所有在较小度量距离内且来自相反方向的行人的影响：而在kNN逆流模型中，一个选择的行为方向受到来自相反方向的k个最近邻的固定数量分布的影响，捕获自组织车道形成，并研究通道中形成车道数量的影响因素。结果表明，随着密度的变化，kNN计数流模型中的车道形成模式几乎相同，而基于度量距离的模型则不同，这意味着kNN相互作用在行人集体现象的出现中起着更基本的作用。通过将车道形成模式和基本图与真实行人逆流进行比较，进一步验证了kNN逆流模型。讨论了车道形成和流量提高的原因，研究了模型的平均速度、占用率和总入口密度之间的关系。结果表明，kNN相互作用提供了更有效的交通条件，并且能够量化高密度行人交通的隔离和相变等特征(C) 2010 Elsevier B.V.版权所有
A recent Field study confirmed that animal crowd behavior is dominated by the interaction from the k-Nearest-Neighbors rather than all the neighbors in a given metric distance For the reason that systems with local interaction perform similar self-organized phenomena, we in this paper build two models. i.e., a metric distance based model and a k-Nearest-Neighbor (kNN) counterflow model, based on a simple discrete cellular automaton model emit led the basic model, to investigate the fundamental interaction ruling pedestrian counter flow. Pedestrians move in a long channel and as a result are divided into left moving pedestrians and right moving pedestrians. These pedestrians interact with each other in different forms in different models In the metric distance based model, ones direction of chosen behavior is influenced by all those who are in a small metric distance and come from the opposite direction: while in the kNN counterflow model, ones direction of chosen behavior is influenced by the distribution of a fixed number of the k-Nearest neighbors coming from the opposite direction The self-organized lane formation is captured and factors affecting the number of lanes formed in the channel are investigated. Results imply that with varying density, the lane formation pattern is almost the same in the kNN count erflow model while it is not in the case of metric distance based model This means that the kNN interaction plays a more fundamental role in the emergence of collective pedestrian phenomena Then the kNN counterflow model is further validated by comparing the lane formation pattern and the fundamental diagram with real pedestrian counter flow. Reasons for the lane formation and improvement of flow rate are discussed The relations among mean velocity, occupancy and total entrance density of the model are also studied. The results indicate that the kNN interaction provides a more efficient traffic condition, and is able to quantify features such as segregation and phase transition at high density of pedestrian traffic (C) 2010 Elsevier B.V. All rights reserved