Evacuating with Others Virtually

点击翻译按钮获取中文摘要

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.