大面积航班延误下群体拥挤事件预警研究

发布时间：2018-03-11 12:36

本文选题：群体拥挤事件　切入点：事故因果连锁理论　出处：《中国民航大学》2016年硕士论文　论文类型：学位论文

【摘要】：根据民航局统计结果显示我国发生大面积航班延误的次数逐年增加。大面积航班延误下航站楼内聚集大量人群,一旦发生群体拥挤事件就会威胁到旅客生命安全。通过对大面积航班延误下群体拥挤事件的预警研究,能够及时预防和控制事件的发生。本文运用事故因果连锁理论从管理失误、旅客心理因素、旅客不安全行为、诱因事件四个方面分析得出航站楼内群体拥挤事件致因机理。根据人流量将航站楼内群体分为自由流、最大流、间歇流、拥挤流四种状态。通过分析群体的自组织和耗散结构特点,得出系统中存在的熵。运用熵的局域平衡假设,将局部人群压力和人群流量作为广义力和广义流,建立群体熵值产生模型和不同状态群体的熵值模型,最终对包含不同状态群体的系统熵值模型进行了构建。通过监控数据计算得出群体系统熵值产生p,根据不同状态群体在理论和实际中所占系统面积的大小,得出群体系统的理论熵值S理论和实际熵值S实际。根据S理论值的大小和p值的正负,将群体拥挤预警划分为红、橙、黄三个等级。根据人群密度得出S实际和S理论的值并比较其大小及的正负来判定启动的预警等级。群体拥挤预警熵值模型不仅能够预测群体拥挤事件发生的地点和时间,而且可以判断系统状态变化的方向和快慢。
[Abstract]:According to the statistics of the Civil Aviation Administration, the number of flight delays in a large area in China has increased year by year. A large number of people are gathered in the terminal building under the large-scale flight delays. Once a crowd congestion event occurs, it will threaten the safety of passengers. Can prevent and control the occurrence of events in time. This paper applies the theory of accident causality chain from management mistakes, psychological factors of passengers, unsafe behavior of passengers, According to the flow of people, the group in the terminal can be divided into free flow, maximum flow and intermittent flow. By analyzing the characteristics of population self-organization and dissipative structure, the entropy in the system is obtained. By using the local equilibrium assumption of entropy, the local crowd pressure and flow are regarded as generalized force and generalized flow. The generation model of population entropy and the model of entropy value of groups with different states are established. Finally, the system entropy model including different state groups is constructed, and the population system entropy value is calculated by monitoring data, which is based on the size of system area occupied by different state groups in theory and practice. The theoretical entropy S theory and the actual entropy S theory of group system are obtained. According to the magnitude of S theory value and the positive or negative value of p value, the crowd crowding early warning is divided into red and orange. According to the population density, the values of S and S theory are obtained and compared with the positive and negative values to determine the early warning level. The entropy model of crowd congestion warning can not only predict the location and time of crowd congestion events. Moreover, the direction and speed of system state change can be judged.
【学位授予单位】：中国民航大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：X949;V35

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