超市的应急疏散具有路线复杂、人员密度大的特点,是安全应急疏散领域需要解决的核心关键问题之一。本文基于社会力模型与A星算法建立了人员应急疏散模型,解决了社会力模型存在的人员重叠和非现实轨迹问题,研究表明,区别于SFPE(society o...超市的应急疏散具有路线复杂、人员密度大的特点,是安全应急疏散领域需要解决的核心关键问题之一。本文基于社会力模型与A星算法建立了人员应急疏散模型,解决了社会力模型存在的人员重叠和非现实轨迹问题,研究表明,区别于SFPE(society of fire protection engineers)模式中的人员重叠现象和Steering模式中的人员绕行现象,所提出的模型符合实际的紧急疏散行为。应用本文模型对某超市3种出口处货架、展示台平面布局进行建模分析,结果表明,货架纵向布局及布置展示台疏散时间都较横向布局短;不同布置主要影响出口区域人员密度,造成疏散效果不同。展开更多
In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determine...In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determined by three factors： i） attraction to the local object position （x^-io＋） which is decided by the local information about the individuals＇ position that individual i can find; ii） repulsion from the other individuals on short distances; and iii） attraction to the global object position （xgoal） or repulsion from the obstacles in the environment, The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive.展开更多
文摘超市的应急疏散具有路线复杂、人员密度大的特点,是安全应急疏散领域需要解决的核心关键问题之一。本文基于社会力模型与A星算法建立了人员应急疏散模型,解决了社会力模型存在的人员重叠和非现实轨迹问题,研究表明,区别于SFPE(society of fire protection engineers)模式中的人员重叠现象和Steering模式中的人员绕行现象,所提出的模型符合实际的紧急疏散行为。应用本文模型对某超市3种出口处货架、展示台平面布局进行建模分析,结果表明,货架纵向布局及布置展示台疏散时间都较横向布局短;不同布置主要影响出口区域人员密度,造成疏散效果不同。
基金This work was supported by the National Natural Science Foundation of China (No. 60574088).
文摘In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determined by three factors： i） attraction to the local object position （x^-io＋） which is decided by the local information about the individuals＇ position that individual i can find; ii） repulsion from the other individuals on short distances; and iii） attraction to the global object position （xgoal） or repulsion from the obstacles in the environment, The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive.
