Evacuating with Others Virtually

在火灾/烟雾警报、化学品泄漏警报、炸弹威胁等触发下，现代建筑每天都面临紧急疏散。虽然这些案件大多被证明是虚惊一场，大多数疏散都是有序的，但也有一些悲惨的结局，给涉事者和社会带来了巨大的代价。疏散过程中的困难包括决定在非常有限的时间内做什么，但后果严重，了解威胁的动态性质(例如，火灾/烟雾或化学品泄漏的传播)，以及找到不过度拥挤的安全通道。贯穿这些问题的是，所有此类疏散都涉及多名疏散人员的同时行动。因此，人类在压力下的社会心理行为是至关重要的，迫切需要设计和进行受控环境下的社会心理实验，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的足够引人入胜的方式--通过跑步机和生理评估来提交科学测试假设，否则这些假设是不可能通过实验进行测试的。此外，我们将建立数学疏散模型，考虑到实验中确定的关键社会心理特征，以预测疏散人员在与他人一起疏散时的行为。然后将开发新一代优化方法，以确定如何以计算效率高的方式将撤离者有效地引导到安全地点。模型和方法将通过模拟、虚拟现实实验和消防演习进行部分验证。通过汇聚一个由工程师、社会心理学家和感知心理学家以及安全问题专家组成的跨学科团队，该项目将建立关于疏散过程中社会影响的经过充分测试的原则，以制定一个疏散计划，该计划可以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地方，无论是出口还是避火区。该项目还将产生更广泛的影响，为教育学生、专业人士和社区安全疏散提供独特而丰富的环境。具体地说，VR实验将在研究中包含两个部分。在第一部分，个体参与者实验将对信息和社会影响过程被假设为影响疏散的几种方式进行严格的测试。在第二部分，小组参与者实验将提供一个适度受控的环境，用于探索疏散人员群体疏散时发生某些现象的条件，这些现象不可能通过只关注单个参与者来检查，也不可能以其他方式观看展开，除非在不受控制的环境中，如实际的灾难。要审查的关键问题包括关于基于信息的焦虑减轻的假设、对环境的熟悉(或习惯性疏散倾向)、羊群(或拉拢他人)、对指导的反应以及社会纽带因素。将建立数学疏散公式，以捕捉虚拟现实实验中确定的社会心理特征的本质。然后，将开发新一代随机混合整数优化方法，以使用我们在分解和协调框架内的最新代理拉格朗日松弛，以计算高效的方式优化制导。将单独优化个别小组的出口路线，并协调由此产生的路线，以便适当地共享容量有限的通道和房间，以满足联合行动的总需求。然后，模拟、虚拟现实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的出现和消散，并检查实验操作如何影响这些现象和其他现象。我们还希望能够对这种现象进行建模和预测，并开发优化的解决方案，以缓解堵塞和其他有害行为，从而实现有效疏散。

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.