Building Emergency Evacuation: Innovative Modeling and Optimization

建筑紧急疏散：创新建模与优化

• 批准号: 1000495
• 负责人: Peter Luh
• 金额: $ 49.85万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000495&HistoricalAwards=false
• 关键词: Building; Emergency; Evacuation; Innovative; Modeling

Many lessons have been learned on building emergency evacuation, with perhaps the steepest advances made through detailed analyses of the large-scale evacuations involved in events such as 1993 and 2001 World Trade Center attacks. Such studies have identified several key features of crowd evacuation behaviors, including significant delays before evacuation and the few factors that affect evacuation routes. Also, crowd disorder and blocking were well observed in events such as the nightclub fires of Rhode Island in 2003 and of Bangkok in 2009. These features have been explained through psychological theories and models, and viewed as crucial determinants for the evacuees? survival in emergency evacuation. However, one critical gap is that the wealth of such psychology-related knowledge encapsulated in the diverse and complex array of theories, models and simulation has not been integrated into the current methods for emergency evacuation. Consequently, intuitions are mostly used regarding the potential consequences of providing one set of guidance versus another to evacuees, and psychological factors that affect crowd behaviors are not systematically considered. With recent technological innovations on fire detection, crowd communication and guidance in emergencies, it is important to redress the serious gap between the state-of-the-art knowledge and our ability to effectively guide crowds to safety. In the proposed research, an innovative model of crowds will be developed to incorporate key psychological behaviors of evacuees, including initiation delay, way-finding, and disorder and blocking. It will describe how situation information (e.g., perceived hazard and guidance received) changes crowd behaviors in the collective or group sense (e.g., crowd flow delays, directions, and rates), and enable the prediction of crowd movement. An optimization problem will be established to evacuate as many people and as fast as possible while reducing relevant risks through appropriate guidance on crowds by using, for example, dynamic exit signs or audio announcements. To efficiently solve this time-critical problem, advanced optimization methods will be developed and synergistically integrated within a divide-and-conquer approach to generate effective guidance. The models and methods will then be used to generate virtual reality experiments from the first-person perspective to validate psychological behaviors of participants. In addition, a Wiki-based platform will be developed to test, validate, and enhance models and methods through open sourcing of various modules and sharing of lessons learned for broad participation and impact. With the involvement of Wiki-based platform participants, the research will have broader impacts on education, academic research, and engineering use; and the applications of the models and methods to other similar problems, e.g., emergency management of high school or university events or emergency evacuation of cities or regions. Furthermore, the research will be thesis topics for participating Ph.D. and MS students and special projects for undergraduate students, educating the next generation of engineers, psychologists, and homeland security leaders. Special efforts will be made to recruit minority and female students, including but are not limited to our participations in UConn School of Engineering?s ?da Vinci? program designed for high school mathematics and science teachers and counselor, the ?Multiply Your Options? program designed for 8th grade middle school female students, and the ?Engineering 2000? program designed for high school juniors and seniors. Our goals are to establish sound theory and methods for innovative modeling and optimization of time-critical and high-stake events while attracting and educating students. Ultimately, the proposed research will benefit society by saving lives and reducing injuries through better designed or configured evacuation systems, and through optimized crowd guidance in real time.

关于建立紧急撤离的许多教训，也许是通过对1993年和2001年世界贸易中心攻击等事件中涉及的大规模撤离的详细分析所取得的最大进步。 这样的研究已经确定了人群疏散行为的几个关键特征，包括疏散前的重大延迟以及影响疏散路线的几个因素。 此外，在2003年罗德岛的夜总会大火和2009年的曼谷夜总会大火中，人们对人群障碍和阻塞进行了很好的观察。这些特征已经通过心理理论和模型来解释，并被视为撤离者的关键决定因素吗？紧急撤离的生存。 但是，一个关键的差距是，这种与心理相关的知识的财富封装在各种各样且复杂的理论，模型和模拟中，尚未集成到当前的紧急撤离方法中。 因此，直觉主要用于提供一组指导的潜在后果，而另一种指导是撤离者的，而影响人群行为的心理因素也不是系统地考虑的。 借助有关火灾检测，人群沟通和紧急情况指导的最新技术创新，重要的是要纠正最先进的知识与我们有效地指导人群安全的能力之间的严重差距。 在拟议的研究中，将开发出一种创新的人群模型，以结合撤离者的关键心理行为，包括启动延迟，寻路，混乱和阻塞。 它将描述情况信息（例如，感知到的危害和指导）如何改变集体或群体意义（例如人群流量延迟，方向和费率）的行为，并实现人群运动的预测。 将建立一个优化问题，以撤离尽可能多的人，尽可能快地通过通过对人群的适当指导来减少相关风险，例如使用动态出口标志或音频公告。 为了有效地解决这一关键时期问题，将开发高级优化方法并协同整合在分裂和拼接方法中以生成有效的指导。 然后，这些模型和方法将用于从第一人称角度生成虚拟现实实验，以验证参与者的心理行为。 此外，将开发一个基于Wiki的平台来测试，验证和增强模型和方法，通过开放采购各种模块，并共享经验教训以进行广泛参与和影响。 随着基于Wiki的平台参与者的参与，该研究将对教育，学术研究和工程使用产生更大的影响；以及模型和方法在其他类似问题中的应用，例如高中或大学活动的紧急管理或城市或地区的紧急撤离。 此外，该研究将是参与博士学位的论文主题。 MS学生和本科生的特殊项目，教育下一代工程师，心理学家和国土安全部领导者。 将为招募少数民族和女学生做出特别的努力，包括但不限于我们参加UConn工程学院的参与？为高中数学和科学老师和辅导员设计的计划，您的选择倍增吗？为8年级的中学女学生和“工程2000”设计的计划？专为高中大三学生和老年人设计的计划。 我们的目标是在吸引和教育学生的同时，建立合理的理论和方法，以创新建模和优化时间关键和高级事件。 最终，拟议的研究将通过挽救生命并通过设计更好或配置的疏散系统以及实时优化的人群指导来挽救生命并减少伤害，从而使社会受益。