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Investigation of crowd's aggregation measurement based on an entropy model

机译:基于熵模型的人群聚集测量研究

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摘要

Mass events have led to many deadly crowd crushes and crowd disasters. In order to avoid crowd accidents, a number of the studies have been conducted. For the people in public place, even a low density crowd can cause risk due to individual's local aggregation. The local aggregation process is the crowd distribution of deviating from random distribution process. Therefore, it is particularly important to measure the degree of randomness or degree of aggregation of crowd distribution. Entropy can be used to quantitatively describe the number of microscopic states in a certain macroscopic state. A crowd entropy model is built to reveal the degree of aggregation crowd or the degree of deviation from random distribution based on entropy difference. The fluctuation of the entropy is used to reflect the disturbance caused by random movement of individual. The research shows that we can judge whether the crowd is aggregating by comparing the entropy difference and the fluctuation value under a certain confidence level. The entropy variation regularity shows that the larger the entropy difference is, the more obvious the crowd aggregation is. It will provide a new perspective and tool for crowd managers to monitor the changes in the degree of crowd gathering.
机译:群众活动导致了许多致命的人群粉碎和人群灾害。为了避免人群意外,已经进行了许多研究。对于公共场所的人民,即使是低密度的人群也可能导致个人的当地聚合导致风险。本地聚合过程是偏离随机分布过程的人群分布。因此,衡量人群分布的随机性或聚集程度尤为重要。熵可用于定量地描述某一宏观状态中的微观状态的数量。建立了人群熵模型,以揭示聚集人群或基于熵差的随机分布的偏差程度。熵的波动用于反映个体随机运动引起的干扰。研究表明,我们可以通过比较熵差和在一定置信水平下的波动值来判断人群是否汇总。熵变化规律表明,熵差异越大,人群聚集越明显。它将为人群经理提供新的视角和工具,以监测人群聚集程度的变化。

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