基于物理的真实感火灾疏散仿真

人员的疏散是目前防火防灾研究的热点,但是传统的人员疏散研究多集中于人群疏散的离散仿真及可视化显示,无法逼真地表现个体的疏散行为.从火灾时人员疏散的物理特点出发,考虑不同人在火灾情况下的行为特征,采取几何连续的疏散空间,可较准确地计算人员安全疏散的时间及路径;建立了建筑物、火焰和角色动画的三维模型,动态交互地模拟火灾疏散过程,仿真效果逼真.     

基于Agent的人员疏散系统设计与实现

通过分析Agent与社会力模型在人员疏散方面的应用及疏散模拟中人和建筑物对象的存储方式,利用Agent技术结合人员疏散的动力学模型、改进的路径规划算法和人员在一般情况下的心理因素以及建筑物的不同特点,建立能够正确反映疏散时人员行为的Agent模型,运用C++语言实现基于Agent的人员疏散模拟系统。疏散结果显示,该系统能够较真实地模拟人员疏散过程。     

基于计算实验的煤矿火灾人员疏散研究

人员疏散是煤矿火灾应急管理的重要研究内容,然而由于其复杂性、真实实验研究的困难性,研究领域尚未对煤矿中火灾人员的疏散进行探究。基于此,提出一种基于计算实验的研究方法。采用Agent建模技术搭建计算实验环境,通过局部微观Agent实体之间相互作用的集合,以三维模拟仿真的形式将难以设想的事故发展情况动态、实时、量化地展现出来,利用计算实验着重分析、评估火灾发生地点和巷道矿工数量对人员疏散的影响。结论:火灾发生地点和巷道矿工数量对人员疏散的时间产生影响,会导致疏散时间增加。     

基于人工势场和Agent的人员疏散系统仿真

从聚众场合火灾中疏散群体的个体角度出发,结合人工势场和Agent,提出了微观的仿真模型。该模型首先将场化影响人行为的火、门和障碍物等因素量化,构建了人工势场体系,其次以个体为对象,通过受力分析确定人员移动方向,最后通过是否开启安全通道,有无人员疏导的分析对比,合理仿真了人员疏散情形。基于Visual C++实现了疏散仿真系统,通过仿真结果分析,该系统能够较真实地模拟人员疏散过程。     

基于FDS的火灾仿真研究

研究火灾中人员优化疏散、减少损失问题,为获取各种火灾数据,克服实际火灾数据获取困难及体现疏散过程中人员的个体差异性,研究了在公共火灾中的疏散方法,并构建了具有个体差异性的人员疏散模型.研究了火灾仿真软件及基本控制方程.利用FDS( Fire Dynamics Simulator)火灾研究工具软件可较为准确快速地分析复杂的三维火灾问题.以某KTV场所为应用实例,研究FDS的具体应用方法.根据FDS火灾数据,在Java环境下实现了上述模型,并进行了相应的仿真验证.结果表明可为火灾研究和人员疏散提供手段,并对建筑物的布局设计具有一定的指导意义.     

基于多智能体的森林火灾人员疏散仿真

森林火灾发生时, 对在林火蔓延过程中灾区人员疏散进行建模与仿真能够检查疏散方案是否合理, 从而为合理救灾提供指导意义. 目前对人员疏散模拟与仿真虽然有很多研究, 但对于森林火灾下人员疏散尚缺乏合适的仿真模型, 给科学救助决策带来很大困难. 为此, 提出将多智能体引入森林火灾蔓延及灾区人员疏散模型研究中, 并借助于Repast平台进行仿真, 实验结果表明本文方法能够为森林火灾人员疏散模拟与仿真提供正确的解决途径.     

基于元胞自动机模型的人员疏散仿真研究

公共安全是经济发展和社会稳定的基础。近年来由于火灾、地震、恐怖活动等突发因素诱发的公共场所人员安全事故屡见报道,在公共场所的安全设计中,安全疏散性能已成为至关重要的因素。为解决上述问题,人员疏散的计算机仿真对提高公共场所的安全疏散性能具有重要意义。对于出口距离最近的原则建立人员疏散的二维随机元胞自动机模型,其中考虑人员绕行的影响,并利用Matlab软件编写了疏散过程的程序,对大空间疏散人群进了仿真,得到了人员疏散基本规律及疏散时间。研究表明:考虑人员绕行的模型更能体现真实的疏散情景。     

基于Agent和元胞自动机的人员疏散过程建模与仿真

人员疏散是一个与人们的实际生活紧密相关的、典型的公共安全问题,对人员疏散过程的研究有利于合理设计建筑布局,保障人民的生命财产安全。本文以某商场为仿真对象,基于Agent和元胞自动机对人员进行建模,通过仿真其疏散过程,比较得出影响人员疏散速度的主要因素。     

宏观场景指导下的微观agent人员疏散仿真模型*

现有的微观agent模型难以描述人群分布和人群运动对疏散过程的影响,为此建立宏观场景指导下的微观agent人员疏散仿真模型,并考虑疏散过程中“推挤—穿行”行为的影响。该模型重点模拟了宏观场景中人群分布及“推挤—穿行”对人员疏散的影响。仿真实验表明该模型可以帮助agent选择更合适的疏散路线,并验证了疏散过程中发生“推挤—穿行”将会延长疏散时间,结果比较符合实际,证明了该模型的合理性和适用性。     

基于社会力模型的人员逆行行为仿真研究

为了对大型场馆疏散过程中出现的人员逆行行为问题进行研究，基于社会力模型建立了人员疏散仿真模型，研究逆行行为对场馆内人群疏散结果的影响。实验结果表明，该模型能够再现疏散过程中人员表现出来的逆行行为。     

An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster

A key activity in emergency management is planning and preparation for disaster. If the right safety measures are implemented beforehand, harmful effects can be significantly mitigated. However, evaluation and selection of effective measures is difficult due to the numerous scenarios that exist in most emergency environments coupled with the high associated cost of testing such scenarios. An agent-based system employs a computational model of autonomous interacting agents in an environment with the purpose of assessing the emergent behavior of the group. This paper presents a prototype of a computer simulation and decision support system that uses agent-based modeling to simulate crowd evacuation in the presence of a fire disaster and provides for testing of multiple disaster scenarios at virtually no cost. The prototype is unique in the current literature as it is specifically designed to simulate a concert venue setting such as a stadium or auditorium and is highly configurable allowing for user definition of concert venues with any arrangement of seats, pathways, stages, exits, and people as well as the definition of multiple fires with fire and smoke dynamics included.     

A BIM-based simulation framework for fire safety management and investigation of the critical factors affecting human evacuation performance

Fire hazards are a big threat to human life and property safety. The U.S. fire statistics reveal that, in 2017 alone, 1,319,500 fires caused 3400 deaths and 14,670 injuries, which resulted in a loss of $23 billion [1]. Effective evacuation planning in densely occupied buildings should be primarily put in place if both the number of injuries/fatalities and the level of property loss are to be minimized. However, it is not realistic, and is unethical to study human evacuation performance under a burning building. For this reason, computational tools tend to be the best approach for simulating fire growth as well as human response to fire hazards. This study aims to develop a BIM-based simulation framework that implements the Fire Dynamic Simulator (FDS) and agent-based modeling (ABM) for simulating fire growth and evacuation performance for different building layout scenarios. An experimental implementation is conducted to validate the proposed framework, which verified the benefits of (1) using BIM to offer a platform for conducting simulation design and visualizing the simulation results of (a) hazardous fire zones and (b) effective escape routes; (2) simulating fire growth using the FDS tool; (3) developing an agent-based model that accounts for the critical factors affecting evacuation performance; and (4) applying a statistical analysis for investigating the effects of influential parameters from the proposed model. As a result, the simulation outputs can be used to optimize the building design and to investigate the influential factors on human evacuation efficiency. The proposed framework contributes to building fire safety management by enabling to minimize both injuries/fatalities and property loss.     

基于DI-Guy的校园消防训练仿真

针对高校人口密度大、难以高效组织消防训练、缺乏处理应急事件经验等问题,提出一种基于DI-Guy软件的虚拟仿真训练方法。首先利用MultiGen Creator软件建立校园三维模型并加载到DI-Guy的可视化窗口中进行构建,然后模拟火灾发生场景和消防疏散过程,最后对人群疏散方案效果进行数据分析,同时对仿真模拟过程中的一些关键技术,包括利用自定义模型建立、特效实现、人物的行为控制和碰撞检测等进行研究和应用。仿真结果表明,所提出的仿真模型能够合理地反映人群疏散情况,起到良好的展示作用,为校园应急事件在DI-Guy平台的仿真提供可行性方案。     

Modeling group structures in pedestrian crowd simulation

Grouping is a common phenomenon in pedestrian crowds and plays important roles in affecting crowd behavior. Group modeling is still an open challenging problem and has not been incorporated by existing crowd simulation models. Motivated by the need of group modeling for crowd behavior simulation, this paper presents a unified and well-defined framework for modeling the structure aspect of different groups in pedestrian crowds. Both intra-group structure and inter-group relationships are considered and their effects on the crowd behavior are modeled. Based on this framework, an agent-based crowd simulation system is developed and crowd behavior simulations using two different group structures are presented. The simulation results show that the developed framework allows different group structures to be easily modeled. Besides, different group sizes, intra-group structures and inter-group relationships can have significant impacts on crowd behaviors.     

人群应急疏散中一种多智能体情绪感染仿真模型

为了从情绪的视角分析紧急情境下人群的疏散行为,梳理了现有情绪感染的研究工作,总结了人群紧急状况下行为特点.采用智能体描述人群个体,提出一种多智能体情绪感染模型.其主体框架分为感知层、情绪层、感染层、行为层和行动层.归纳了产生情绪感染现象的3个条件及情绪感染的3个规则,提出了情绪感染的算法,考虑个体的个性和个体间距离因素,采用情绪强度和人群紧密度来计算个体疏散速度.用C#语言编制了仿真实验,采用真实的地震疏散案例,验证了仿真疏散时间和实际观测的基本一致.通过与以往基于传染病思路的情绪感染模型对比,所提出的模型可以更好地描述情绪感染从局部到整体的过程.实验结果表明,所提出的模型可以推演情绪驱动下的群体聚集行为,有望为制定应急疏散预案提供一种可视化分析方法.     

人群应急疏散可视仿真研究进展和问题

人群应急疏散可视仿真是用智能体来模拟具有自主感知、情绪和行为能力的人群个体,并采用3维可视的方式来直观呈现人群应急疏散情景,可以为制定人群应急预案提供形象直观的分析方法。本文从人群仿真数据的来源、人群导航模型的构建、人群行为模型、人群情绪感染、人群渲染5个方面概述目前研究的进展,然后从仿真模型的可验证性、人群疏散导航模型的构建、人与环境的物理模型、动物逃生实验与仿真、疏散中的社会行为表现以及人群情绪的可视计算6个角度讨论需要进一步研究的问题。针对需要深入研究的问题,指出借助于紧急事件的视频监控分析和虚拟人群情景的用户调查,有助于完善人群仿真模型。结合物理模型,可以更准确地描述人群应急疏散场景。开展动物逃生实验分析,有助于完善人群运动导航算法。建立人群社会行为模型,可以更详细描述疏散中人群行为的多样性。构建基于多通道感知的人群情绪感染计算方法,可以详尽描述情绪感染的过程。人群应急疏散行为的可视仿真研究在城市的安全管理方面具有重要的应用前景,但其研究仍存在很多亟待解决的问题,综合地运用多学科知识,完善实验手段是进一步推动研究的关键所在。     

Crowd simulation for emergency response using BDI agents based on immersive virtual reality

This paper presents a novel methodology involving a Virtual Reality (VR)-based Belief, Desire, and Intention (BDI) software agent to construct crowd simulation and demonstrates the use of the same for crowd evacuation management under terrorist bomb attacks in public areas. The proposed BDI agent framework allows modeling of human behavior with a high degree of fidelity. The realistic attributes that govern the BDI characteristics of the agent are reverse-engineered by conducting human-in-the-loop experiments in the VR-based Cave Automatic Virtual Environment (CAVE). To enhance generality and interoperability of the proposed crowd simulation modeling scheme, input data models have been developed to define environment attributes (e.g., maps, demographics, evacuation management parameters). The validity of the proposed data models are tested with two different evacuation scenarios. Finally, experiments are conducted to demonstrate the effect of various crowd evacuation management parameters on the key performance indicators in the evacuation scenario such as crowd evacuation rate and densities. The results reveal that constructed simulation can be used as an effective emergency management tool.     

Behavioral,data-driven,agent-based evacuation simulation for building safety design using machine learning and discrete choice models

To improve occupant safety during building emergencies, evacuation simulations have been widely used for building safety design. Since occupant behavior is a determining factor for the outcome of building emergencies, accurately capturing how occupants make decisions and integrating occupants’ decision-making processes in evacuation simulations is important. In this study, based on the results of fire evacuation experiments in a virtual metro station, how different social (crowd flow) and environmental (visual access and vertical movement) factors would affect individuals’ wayfinding behavior was predicted using machine learning and discrete choice models. The trained models were further employed in agent-based evacuation simulations to examine crowd evacuation performance under different building design scenarios. Both the machine learning and discrete choice models could accurately predict individuals’ directional choices during emergency evacuations. Different building attributes could collectively influence occupant behavior, leading to distinct exit choices and evacuation times. While both the trained machine learning and discrete choice models generated similar results, the discrete choice model had better interpretability. Moreover, by comparing the trained models in this study with a model developed in a prior study, it was found that agents had significantly distinct responses to different building designs. Critical factors (e.g., type and size of buildings, occupants’ familiarity with the building) for the applicability of evacuation models were identified. Furthermore, recommendations were provided for future research that aims at employing evacuation simulations for building design evaluation and optimization.