今天搜一篇文章的时候发现我的博士论文在中国知网已经可以检索到了：

相向行人流自组织行为机理研究
Study of the Behavioral Mechanism of Self-organized Pedestrian Counter Flow

【作者】马剑

【导师】廖光煊; 卢兆明

【作者基本信息】中国科学技术大学，安全技术及工程，2010年，博士

【摘要】 拥挤人群的运动,包含单向、双向、交叉、往复等形式,可能引发诸如踩踏之类的严重事故。在这类事故中,行人可能遭受身体伤害甚至失去生命。因此,建筑设施内行人运动的舒适性及安全性问题日益引起建筑设计及管理人员的关注。已有的研究表明,影响建筑服务水平(Level of Service)的因素和行人流自组织的模式紧密相关。但是,目前尚缺乏对导致行人流自组织的行人行为机理的研究。本文中,我们首先开展可控的实验,研究通道内行人运动的微观特征。基于数字图像处理的方法,提取出实验录像中行人的运动轨迹,并对这些运动轨迹做直接线性变换得到相应的现实坐标。依据行人的现实坐标研究单个行人的运动特征、行人与走廊间的作用以及行人与行人间的相互作用。结果表明,当在通道中运动时,典型学生行人的平均弛豫时间为0.71秒,平均最大行走速度为1.51米／秒。同时,行人在运动的过程中与墙壁保持一定的距离。当与墙之间的距离太近时,行人有朝远离墙壁方向运动以避免潜在碰撞的趋势。这种运动趋势最终被量化为行人与墙壁间随距离增长指数衰减的力。当通道内一个运动的行人规避在其运动方向上其他静止行人时,他们间的作用力呈现出各向异性的特点。典型中...更多国学生行人在运动过程中有显著的右行偏向。进一步定量的分析表明,当静止的行人处于运动行人的右前方,那么其对运动行人的力变化不明显；当静止行人处于运动行人的左前方,其对运动行人的力随他们间距离的增加变化显著。其次,对于环形通道内单列同向运动的行人间相互作用的分析则表明,运动个体受其直接前驱最近邻的影响显著,而几乎不受其他行人影响。基于上述实验结果,我们建立κ近邻模型和全近邻模型两个模型以研究相向行人流中运动个体间的基本作用。这两个模型的更新规则与随机行走元胞自动机模型(即基本模型)一致：通道内运动的个体依照其运动方向被分成左行行人和右行行人两种。不同的是,行人因不同的相互作用形式而具有不同的方向选择行为。在全近邻模型中,运动个体的方向选择行为受到距其一定距离范围内迎面.而来的所有的运动个体的影响；在κ近邻模型中,运动个体的方向选择则受到从对面走来的与其距离最近的κ近个体的分布的影响。通过研究相向行人流中自发形成的行人分层以及影响通道内行人分层数目的因素,我们发现随着通道内行人密度的变化,全近邻模型中行人分层的模式变化显著,而κ近邻模型中行人分层模式基本不变且与实际观测相符。这意味着κ近邻作用在行人集群行为涌现中起着更基本的作用。基于演化博弈理论,我们分析了模型中行人分层的涌现机制。并研究了通道内行人的平均速度,密度与边界密度间的关系,讨论了速度、流量增强的原因。通过比较模型中行人分层的模式与实际观测的相向行人流模式、模型计算得到的基本图与行人流经验基本图,我们对κ近邻模型进行了验证。结果表明κ近邻模型可以改善交通环境,进而增加自由流区域内行人的运动速度,并且能够重现高密度时的相分离及自由流向壅塞流转变的特征。考虑到实际行人流中行人相对.位置有错位的现象,我们对κ近邻模型进行了改进,建立了多格子κ近邻模型,并采用多格子κ近邻模型研究了相向行人流受系统尺寸、行人视野范围以及非对称边界条件等影响下的动力学行为。在深入理解相向行人流中行人行为机理的基础上,我们将κ近邻模型嵌入地理信息系统(GIS)平台,开发了基于GIS的行人流模拟软件。并进一步以不同的车站站台设计方案为例,讨论为改善行人交通环境采取不同拥挤人群控制措施时,地铁站台上行人运动的基本图以及实时的设施服务水平图,评估人行设施的服务水平,研究探讨拥挤人群的控制方法。  还原

【Abstract】 Pedestrian crowd movement, including single direction, bi-directional flow etc, may trigger serious crowd disasters such as trampling. People may be injured or even killed in these disasters. As a result, the effect of building facilities on the comfort and safety of people's movement becomes one of the important concerns of building designers and facility managers. Factors affecting the level of service relate closely to pedestrian flow pattern. Previous studies indicate that self-organized patterns emerging in pedestrian counter flows may affect the flow rate and velocity of crowds. However, the studies rarely discussed the inter-personal interaction in pedestrians.In this study, we first performed well-controlled experiments to capture the moving characteristics of pedestrians in a corridor. Pedestrians'moving trajectories were first extracted with digital image processing method and then mapped into real space coordinates by adopting a direct linear transformation approach. Moving ...更多characteristics of single pedestrian, interaction between pedestrian and the corridor as well as interaction between pair pedestrians were analyzed. It was found that when walking in the corridor, the average relaxation time of typical Chinese pedestrians was about 0.71s, and the maximum mean velocity of free walking was about 1.51m/s. Meanwhile, these pedestrians also kept a suitable distance to the wall to avoid potential collisions. When walking too close to the wall, the pedestrian had a tendency to walk away. This phenomenon was then expressed as an exponential decay force function. When one pedestrian tried to evade another standing still pedestrian in the corridor, the interaction between them showed a non-isotropic feature. The experimental results indicated that the participants preferred to walk with right preference more significantly. We further quantified the interaction among pedestrians, and found that the force from those who located on the right-forward direction did not change much while from those who located on the left-forward direction did vary with the increase of distance. Interaction among pedestrians in a single file uni-directional flow show that the moving pedestrian is affected by his direct predecessor most while is barely affected by others.Based on the experimental findings, two models were established, namely a metric distance based model and a k-Nearest-Neighbor (kNN) counterflow model, which could be used to investigate the fundamental interaction ruling pedestrian counter. flow. The basic update schemes of these two models were the same with a cellular automaton (CA) random walker model, which is entitled as basic model hereafter. Pedestrians moving in a long channel will evolve 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, the direction chosen behavior of an individual is influenced by all those who are in a small metric distance and come from the opposite direction; while in the kNN counterflow model, the direction chosen behavior of an individual is influenced by the distribution of a fixed number of the k-Nearest neighbors coming from the opposite direction. The self-organized lane formation was captured and factors affecting the number of lanes formed in the channel were investigated. Results implied that with varying the density, the lane formation pattern varies substantially in the case of metric distance based model while is nearly the same in the kNN counterflow model which matches field observations. This means that the kNN interaction plays a more fundamental role in the emergence of collective pedestrian phenomena. The relations among mean velocity, occupancy and total entrance density at the boundaries of the counter flow system were also studied. Reasons for the lane formation in the CA models were theoretically investigated on the basis of game theory. Reasons for the velocity enhancement and flow improvement were also discussed.The kNN counterflow model was further validated by comparing lane formation pattern and the fundamental diagram with real pedestrian counter flow. The results indicated that the kNN interaction enhances the mean velocity in the free flow phase by providing more efficient traffic condition, and is able to quantify features such as segregation and phase transition at high density of pedestrian traffic. Considering the facts such as the pedestrians'locations are out of alignment in reality, we further modified the kNN model into multi-grid kNN model to mimic pedestrian flow. Dynamics of the multi-grid kNN model were studied to detail traffic characteristics of pedestrian counter flow.With these insights in the behavioral mechanism of pedestrian counter flow, we illustrated the present study in the area of crowd control by an example of improving traffic situation for pedestrian counter flow in a long corridor in respect of a series of layout design. To facilitate application, we further embed the kNN counterflow model into a Geographic Information System (GIS) platform and try to derive fundamental diagram, as well as real-time level-of-service map so as to evaluate levels of services of pedestrian traffic facilities and efficiencies of different crowd control methods.  还原

转载本文请联系原作者获取授权，同时请注明本文来自马剑科学网博客。
链接地址：https://wap.sciencenet.cn/blog-5422-364228.html

上一篇：非常规突发事件立项
下一篇：会议信息--ICEM2011