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.

由火灾/烟雾警报、化学品泄漏警报、炸弹威胁等触发，现代建筑每天都面临紧急疏散。虽然大多数情况下，这些案件被证明是假警报和大多数疏散是有序的，有些结束悲惨和成本巨大的参与和社会。疏散过程中的困难包括决定在时间非常有限但后果严重的情况下做什么，理解威胁的动态性质（例如，火灾/烟雾或化学品泄漏的传播），并找到不过度拥挤的安全通道。贯穿这些问题的一个事实是，所有这类撤离都涉及多名被撤离者的同时行动。因此，人类在压力下的社会心理行为是至关重要的，并且迫切需要在受控环境下设计和进行社会心理学实验，并制定可验证的理论。这项研究将使用沉浸式虚拟现实（VR）实验-一种将个人嵌入建筑紧急情况中的充分参与方式-通过仿真和生理评估来提交科学测试假设，否则无法通过实验进行测试。此外，我们将建立数学疏散模型，考虑到在实验中确定的关键社会心理特征，以预测疏散人员将如何与他人一起疏散时的行为。然后将开发新一代的优化方法，以确定如何以计算效率高的方式将疏散人员有效地引导到安全地点。模型和方法将通过仿真、VR实验和消防演习进行部分验证。通过汇集工程师，社会和感知心理学家以及安全问题专家的跨学科团队，该项目将建立关于疏散期间社会影响的久经考验的原则，以制定疏散计划，确定可能发生的事情以及如何有效地引导疏散人员到安全的出口或消防避难区。该项目还将产生更广泛的影响，为教育学生、专业人士和社区提供一个独特而丰富的环境，让他们了解安全疏散。具体来说，VR实验将包括两个研究部分。 在第一部分中，个人参与者的实验将提供严格的测试，在信息和社会影响过程已经假设影响疏散的方式。 在第二部分中，小组参与者实验将提供一个适度控制的设置，以探索当疏散人群疏散时会发生某些现象的条件，这些现象不可能通过单独关注单个参与者来检查，除非在不受控制的设置中，例如实际灾难，否则不可能观察到这些现象。要检查的关键问题包括基于信息的焦虑减少，熟悉的环境（或习惯性疏散倾向），羊群效应（或拉与他人的关系），对指导的反应，和社会联系因素的假设。将建立数学疏散公式，以捕捉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整数优化方法，以便在分解和协调框架内使用我们最新的替代拉格朗日松弛法以计算高效的方式优化制导。每个小组的出口路线将单独优化，并协调由此产生的路线，以便适当共享容量有限的通道和房间，以满足联合移动的总需求。仿真、VR实验和消防演习将为模型和方法提供部分验证。我们希望能够看到瓶颈的出现和消失，并研究实验操作如何影响这些和其他现象。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，以实现有效疏散。