Evacuating with Others Virtually

与他人虚拟避难

• 批准号: 1463520
• 负责人: Peter Luh
• 金额: $ 61.91万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1463520&HistoricalAwards=false
• 关键词: Evacuating; Others; Virtually

Triggered by fire/smoke alarms, chemical spill alerts, bomb threats, etc., emergency evacuations are faced daily by modern buildings. Although most of these cases turn out to be false alarms and most evacuations are orderly, some end tragically and cost greatly to those involved and to the society. Difficulties during evacuations include deciding what to do with very limited time but serious consequences, understanding the dynamic nature of threats (e.g., propagation of fire/smoke or chemical spills), and finding safe passages which are not over-crowded. Cutting across these issues is the fact that all such evacuations involve the simultaneous movement of multiple evacuees. As such, human social psychological behaviors under stress are of paramount importance, and there is a critical need to design and conduct social psychological experiments under controlled circumstances and to formulate verifiable theories. This research will use immersive virtual reality (VR) experiments - a sufficiently engaging way of embedding an individual in a building emergency - with treadmills and physiological assessments to submit to scientific test hypotheses that are otherwise impossible to test experimentally. Moreover, we will establish mathematical evacuation models taking into account the key social psychological features identified in the experiments to predict how evacuees will behave when evacuating with others. A new generation of optimization methodology will then be developed to determine how evacuees should be effectively guided to safe locations in a computationally efficient manner. Models and methods will be partially validated through simulation, VR experiments and fire drills. By bringing together a cross-disciplinary team of engineers, social and perception psychologists, and experts in security issues, the project will establish well-tested principles about social influences during evacuation to bear on creating an evacuation program that can determine what is likely to happen and how to effectively guide evacuees to safety, either exits or fire refuge areas. The project will also have the broader impact of providing a unique and fertile context for educating students, professionals and communities about safe evacuations.Specifically, VR experiments will contain two parts in the research. In the first part, individual-participant experiments will provide rigorous tests of a number of ways in which information and social influence processes have been hypothesized to affect evacuation. In the second part, small group participant experiments will offer a moderately controlled setting for exploring the conditions under which certain phenomena will occur when groups of evacuees evacuate, phenomena that are impossible to examine by a focus on individual participants alone, and impossible to otherwise watch unfold except in uncontrolled settings such as actual disasters. Key issues to be examined include hypotheses on information-based anxiety reduction, familiarity of the environment (or habitual evacuation tendencies), herding (or pull to affiliate with others), responses to guidance, and social bond factors. Mathematical evacuation formulations will be established to capture the essence of social psychological features identified in VR experiments. A new generation of stochastic mixed-integer optimization methods will then be developed to optimize guidance in a computationally efficient manner using our latest Surrogate Lagrangian Relaxation within the decomposition and coordination framework. Egress routes for individual groups will be separately optimized, and resulting routes will be coordinated so that passages and rooms with limited capacities are properly shared to meet the total needs for joint movement. Simulation, VR experiments and fire drills will then provide partial validations of models and methods. We expect to be able to see bottlenecks appearing and dissipating, and to examine how experimental manipulations affect these and other phenomena. We also expect to be able to model and predict such phenomena, and develop optimized solutions to alleviate blocking and other detrimental behaviors for effective evacuation.

由火/烟雾报警器，化学溢出警报，炸弹威胁等触发，现代建筑每天都面对紧急撤离。尽管这些案件大多数被证明是错误的警报，并且大多数撤离是有序的，但有些案件可悲，对参与者和社会的人付出了很大的成本。撤离期间的困难包括确定时间非常有限，但后果的严重后果，了解威胁的动态性质（例如，火/烟雾或化学溢出的传播），以及找到没有过度拥挤的安全通道。解决这些问题的事实是，所有这些撤离都涉及多个撤离者的同时运动。因此，在压力下的人类社会心理行为至关重要，并且在受控环境下设计和进行社会心理实验的迫切需要。这项研究将使用沉浸式虚拟现实（VR）实验 - 一种充分引人入胜的方式，将个人嵌入建筑紧急情况 - 跑步机和生理评估来服从科学测试假设，否则这些假设是无法通过实验测试的。此外，我们将在实验中确定的关键社会心理特征来建立数学疏散模型，以预测与他人撤离时撤离者的行为。然后将开发新一代的优化方法，以确定应如何以计算有效的方式有效地引导撤离者到安全位置。模型和方法将通过模拟，VR实验和消防演习进行部分验证。通过将一个跨学科的工程师，社会和感知心理学家以及安全问题的专家组成，该项目将建立经过良好测试的有关撤离期间社会影响的原则，以忍受创建撤离计划，以确定可能发生的事情以及如何有效地指导疏散疏散人员到安全，要么出口或消防难民区。该项目还将产生更广泛的影响，即为教育学生和社区提供有关安全撤离的独特而肥沃的背景。特别是，VR实验将包含研究中的两个部分。 在第一部分中，个人参与者的实验将对已假设信息和社会影响过程的多种方式进行严格的测试，以影响疏散。 在第二部分中，小组参与者实验将提供一个中等控制的环境，以探索探索某些现象的条件，当疏散者撤离群体撤离时，仅仅对单独的参与者的重点就无法检查的现象，并且不可能观察以其他方式观察，除非在不受控制的环境中，例如实际灾难（例如实际灾难）。待检查的关键问题包括有关减少信息焦虑的假设，环境的熟悉程度（或习惯疏散趋势），放牧（或与他人的关联），对指导的反应以及社会纽带因素。将建立数学疏散表格，以捕获VR实验中确定的社会心理特征的本质。然后，将开发新一代的随机混合构成优化方法，以使用我们在分解和协调框架内的最新替代Lagrangian放松，以计算有效的方式优化指导。单个组的出口路线将分别进行优化，并将协调所得的路线，以便适当地共享具有有限能力的通道和房间，以满足关节运动的总需求。然后，模拟，VR实验和消防演习将提供模型和方法的部分验证。我们希望能够看到瓶颈出现和消散，并检查实验操作如何影响这些现象和其他现象。我们还希望能够建模和预测这种现象，并开发优化的解决方案，以减轻阻塞和其他有害行为以有效撤离。