疏散仿真中关注个体心理的路径规划

为了提高虚拟行人在疏散仿真中路径规划的智能性,在微观仿真框架下提出一种基于个体心理的实时路径规划方法.该方法根据真实行人的认知模式建立以个体为中心的环境认知域,动态获取其周围环境的人群密度、危险强度等相关信息;进行路径规划时,采用mental cost综合考虑了疏散时间耗费、路径安全性以及自主行动能力等因素对路径选择的影响,为每个个体生成一条其主观认为"最优"的路径;在疏散过程中,虚拟行人能够根据动态更新的现场信息,通过重规划对初始疏散路径进行调整.基于个体认知域与mental cost在导航图中搜索路径,提出了mental A*搜索算法,可以进行实时规划.最后针对不同疏散场景进行了仿真实验,验证文中方法的有效性与可扩展性.     

基于量子蚁群算法的建筑消防疏散路径规划

摘要：针对现在大空间建筑消防应急疏散问题,在火灾发生时,为撤离人群提供一条从危险区域到安全地带的最短安全路线。对疏散路径优化进行了研究,提出一种融合量子进化算法的改进蚁群算法用于消防疏散路径规划,用量子比特表示信息素,量子旋转门反馈控制信息素更新,即能体现量子并行计算的高效性,又能拥有蚁群算法较好的寻优能力。通过三个基准函数优化仿真与传统量子进化算法进行对比,证明算法较优的性能。再通过路径优化的仿真实验与经典蚁群算法进行比较,结果表明,算法能够有效避免陷入局部最优和拥有更快的收敛速度,在疏散路径规划中更为有效。     

双向蚁群算法的智能消防疏散图路径规划

当前我国正处在城市发展高速时期,综合建筑不断涌现,这时可根据火灾实时信息自动调整疏散路径的智能消防疏散图应运而生。路径规划算法是智能消防疏散图的重点研究方向之一。针对智能消防疏散图需结合火场信息动态规划路径等问题,提出了一种改进双向蚁群算法。添加了双向搜索策略,提高了算法全局搜索能力;结合A*算法改进了初始信息素分布,减小了算法初期搜索盲目性;改进了信息素更新策略,提高了算法收敛速度;结合火场信息和转向惩罚系数对算法蒸发系数、启发函数和转移概率进行改进,降低了陷入局部最优风险,提高了算法搜索效率和路径平滑性,并有效避开了火灾影响区域。通过仿真验证了算法有效性。     

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.     

考虑灾害实时扩散的室内火灾疏散路径选择模型

针对室内火灾中疏散路径的决策问题,基于改进蚁群算法提出考虑火灾实时扩散的最短路径选择模型.建立六边形栅格地图,使每次移动至相邻网格的时间均等,进而实现火灾扩散与人群疏散同步进行;借助火灾动力学软件(FDS)获得火灾扩散参数,分析疏散过程中火灾扩散对疏散路径的实时影响;对多种情景下的仿真算例进行求解,结果表明,所建立模型不但可解决“斜向穿墙”的问题,还可避免出现疏散路径盲目绕远或误入危险区域的不合理现象,并为智能应急疏散系统的研究提供有益参考.     

基于智能体方法的人群疏散三维仿真

研究建筑物在不同出口条件设置下人员的安全疏散问题。针对人群仿真中如何计算疏散路径问题,文中采用导航图技术和agent技术来对个体进行疏散路径的规划。同时为表现人群运动过程当中个体之间的相互碰撞问题,结合碰撞规避规则进行穿透矫正。并且采用三维引擎DeltaSD在Vc＋＋9．0环境下进行仿真并搭建了一套三维人群运动模拟平台。最后分别在三种不同出口条件下的虚拟场景中进行仿真。对比仿真结果表明该平台能直观地展现整个人群疏散的运动过程,并能够对建筑物出口安全设计的合理陛提供有效的数据依据。     

计算机辅助大规模人群疏散平台

针对现有疏散策略的不足,构建一个计算机辅助大规模人群疏散平台,为政府相关部门在城市建设和防灾规划中提供科学决策支持。该平台集成了疏散仿真模型和基于时变动态流问题的路网优化模型,引入组件式GIS技术,采用模块化多层体系结构建立了一个准确、实时的疏散引导系统。     

基于全局路径规划的相互速度障碍物人群疏散方法

针对相互速度障碍物（RVO）模型缺少全局路径规划,只依靠局部碰撞避免不能很好地模拟复杂的疏散场景问题,提出了一种剩余路径代价尽量小的动态全局路径选择方法。该方法包含路径预处理和路径实时更新两部分:第一部分使用快速最短路径算法（SPFA）求取场景最短路径（SSP）;第二部分根据SSP快速动态地计算每个个体的最优疏散路径,并使用KD树优化障碍物阻挡判断过程。最后将方法扩展到多楼层、多障碍物、多通道、多出口的复杂场景实现了近千人的仿真实验。实验结果表明,该方法在多个场景中都取得了良好的路径规划效果。     

基于连续体模型的人群疏散系统的边界控制

研究一类存在扰动的一维人群疏散系统的边界控制问题.以走廊中的人群动态为例,基于数量守恒定律建立人群动态模型;由非线性偏微分方程描述系统模型,并直接在分布参数的范畴内,设计Robin、Neumann、Dirichlet三种边界控制律,用于控制行人在疏散过程中的移动方式,避免拥堵的产生;利用李雅普诺夫方法对边界控制律作用下的人群疏散系统稳定性给出详细证明,并通过一个仿真实例验证边界控制律的有效性.研究成果可以应用到生活中单入口单出口场所的人群动态管理.     

一种动态限制搜索区域的最短路径规划算法*

提出一种动态限制搜索区域的最短路径规划算法,它是根据实际道路网络的空间分布特性,动态限制搜索区域,以降低算法的搜索规模,降低算法的时间复杂度和空间复杂度,提高算法的运行效率.实验证明,对于实际城市道路网络结构相对比较规则的最短路径规划,此算法极大地提高了规划的效率.     

基于地面场粒子群优化算法的高密度人群应急疏散建模

针对非常规突发事件环境下高密度人群的拥挤管理和快速疏散问题,提出一种由感知层、传输层、计算层和应用层构成的多层结构人群疏散信息物理系统（E-CPS）体系框架。在E-CPS体系框架计算层中将静态地面场（FF）建模规则引入经典粒子群优化（PSO）模型,提出地面场PSO （FF-PSO）人群疏散模型,该模型同时具备静态场规则简单、计算快和PSO模型快速搜索、快速收敛的优点。此外,FF-PSO模型中构建了一种新的适应度函数,实现了疏散策略的动态选择,并通过数值仿真及实例仿真验证了FF-PSO模型在拥挤管理中的可行性和有效性。国家会展中心（上海）的实例仿真结果表明,考虑拥堵管理比仅考虑距离最短平均每分钟可多疏散66人,疏散时间节省19 min,疏散效率提升13.4%。     

基于突发事件影响力传播的双向人流疏散仿真

当突发事件发生时,行人行走行为会因为突发事件本身以及突发事件在人群中的传播而改变。对于突发事件影响下的行人行走特征的研究能够提高人流疏散的效率。针对现有研究中数据获取方式的不足,对真实行人场景视频进行图像处理,提取相关数据后分析了无突发事件时行人一般行走特性。针对突发事件下的行人流,利用k-邻近算法和合力的思想描述了突发事件的影响传播和突发事件下行人流的自组织现象,并由此提出一种新的元胞自动机模型,该模型中的行人元胞会受到正常行走、突发事件、安全标识这三个因素所抽象产生的三个作用力的影响。利用模型对突发情况下的双向人流疏散进行仿真,实验结果表明,当安全标识的距离为0、10、20个元胞时,在小范围行人通道中安全标识分布的距离对人群疏散作用不明显;通过对人群间是否存在影响力的研究发现,疏散的效果主要受到附近行人对突发事件传播的影响;突发事件的影响程度太大或影响范围过小都会引发拥堵,不利于人群的疏散。仿真结果与真实世界中的双向行人流疏散情况基本吻合。     

An enhanced model for evacuation vulnerability assessment in urban areas

Rapid urbanization has led to a massive influx of people into cities. When many people congregate in urban areas, crowd crushing emergencies are likely to occur. If vulnerable areas with potential evacuation problems are detected in advance, crowd crushing emergencies may be minimized or even avoided. Thus, an evacuation vulnerability assessment from a precautionary perspective is fundamental. However, the current evacuation vulnerability assessment models are limited in spreading time estimation and evacuation capacity evaluation. To mitigate these limitations, in this study, we propose an enhanced model to quantitatively assess the evacuation vulnerability in urban areas. Our model enhances the current models in two ways. First, we employ a hexagon gridding scheme to construct a network to meet the prerequisite of evacuation spreading at the equal time intervals. Second, we quantify the grid connectivity on the network by considering the grid capacity to avoid underestimation of the evacuation vulnerability. Using a mobile phone location dataset of Shanghai, we systematically investigate the evacuation vulnerability of urban areas in a fine-grained spatio-temporal scale. Areas that may encounter evacuation problems to various degrees can be identified in advance. This information can support emergency management agencies in monitoring dense crowds and ensure early warnings of potential crowd crushing emergencies.     

基于深度Q网络的人群疏散机器人运动规划算法

针对公共场合密集人群在紧急情况下疏散的危险性和效果不理想的问题，提出一种基于深度Q网络（DQN）的人群疏散机器人的运动规划算法。首先通过在原始的社会力模型中加入人机作用力构建出人机社会力模型，从而利用机器人对行人的作用力来影响人群的运动状态；然后基于DQN设计机器人运动规划算法，将原始行人运动状态的图像输入该网络并输出机器人的运动行为，在这个过程中将设计的奖励函数反馈给网络使机器人能够在"环境-行为-奖励"的闭环过程中自主学习；最后经过多次迭代，机器人能够学习在不同初始位置下的最优运动策略，最大限度地提高总疏散人数。在构建的仿真环境里对算法进行训练和评估。实验结果表明，与无机器人的人群疏散算法相比，基于DQN的人群疏散机器人运动规划算法使机器人在三种不同初始位置下将人群疏散效率分别增加了16.41%、10.69%和21.76%，说明该算法能够明显提高单位时间内人群疏散的数量，具有灵活性和有效性。     

Do people follow the crowd in building emergency evacuation? A cross-cultural immersive virtual reality-based study

This study aimed to examine the influence of crowd flow on human evacuation behavior during building fire emergencies, when evacuees perceive high uncertainty in the environment and experience mental stress. Evacuation experiments were conducted in an immersive virtual metro station, in which each participant was presented with one of three different patterns of crowd flow and asked to complete an evacuation task. The patterns of crowd flow were represented by non-player characters that split differently at each wayfinding decision point in the metro station. The experiments were conducted in Beijing, Los Angeles and London. The results showed that uneven splits of crowd flow motivated participants under mental stress to follow the majority of the crowd. This influence of crowd flow was generally consistent over the course of evacuation, and such consistency could be reinforced by stronger directional information conveyed by the crowd flow as well as positive feedback from the outcomes of previous wayfinding decisions. The results also indicated that the influence of crowd flow was significant in all three cultures represented by the three cities, however, the impact of culture on how participants would respond to the directional information conveyed by the crowd flow was insignificant.     

Guided Crowd Evacuation: Approaches and Challenges

Evacuation leaders and/or equipment provide route and exit information for people and guide them to the expected destinations, which could make crowd evacuation more efficient in case of emergency. The purpose of this paper is to provide an overview of recent advances in guided crowd evacuation. Different guided crowd evacuation approaches are classified according to guidance approaches and technologies. A comprehensive analysis and comparison of crowd evacuation with static signage, dynamic signage, trained leader, mobile devices, mobile robot and wireless sensor networks are presented based on a single guidance mode perspective. In addition, the different evacuation guidance systems that use high-tech means such as advanced intelligent monitoring techniques, AI techniques, computer technology and intelligent inducing algorithms are reviewed from a system’s perspective. Future researches in the area of crowd evacuation are also discussed.      

A review of behavior mechanisms and crowd evacuation animation in emergency exercises

Emergency exercises are an efficient approach for preventing serious damage and harm, including loss of life and property and a wide range of adverse social effects, during various public emergencies. Among various factors affecting the value of emergency exercises, including their design, development, conduct, evaluation, and improvement planning, this paper emphasizes the focal role of evacuees and their behavior. We address two concerns: What are the intrinsic reasons behind human behavior? How do we model and exhibit human behavior? We review studies investigating the mechanisms of psychological behavior and crowd evacuation animation. A comprehensive analysis of logical patterns of behavior and crowd evacuation is presented first. The interactive effects of information (objective and subjective), psychology (panic, small groups, and conflicting roles), and six kinds of behavior contribute to a more effective understanding of an emergency scene and assist in making scientific decisions. Based on these studies, a wide range of perspectives on crowd formation and evacuation animation models is summa- rized. Collision avoidance is underlined as a special topic. Finally, this paper highlights some of the technical challenges and key questions to be addressed by future developments in this rapidly developing field.     

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.