965 research outputs found

    Role of opinion sharing on the emergency evacuation dynamics

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    Emergency evacuation is a critical research topic and any improvement to the existing evacuation models will help in improving the safety of the evacuees. Currently, there are evacuation models that have either an accurate movement model or a sophisticated decision model. Individuals in a crowd tend to share and propagate their opinion. This opinion sharing part is either implicitly modeled or entirely overlooked in most of the existing models. Thus, one of the overarching goal of this research is to the study the effect of opinion evolution through an evacuating crowd. First, the opinion evolution in a crowd was modeled mathematically. Next, the results from the analytical model were validated with a simulation model having a simple motion model. To improve the fidelity of the evacuation model, a more realistic movement and decision model were incorporated and the effect of opinion sharing on the evacuation dynamics was studied extensively. Further, individuals with strong inclination towards particular route were introduced and their effect on overall efficiency was studied. Current evacuation guidance algorithms focuses on efficient crowd evacuation. The method of guidance delivery is generally overlooked. This important gap in guidance delivery is addressed next. Additionally, a virtual reality based immersive experiment is designed to study factors affecting individuals\u27 decision making during emergency evacuation

    Development of a Dynamical Egress Behavioural Model under Building Fire Emergency

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    Building fire accidents, as a continuing menace to the society, not only incur enormous property damage but also pose significant threats to human lives. More recently, driven by the rapid population growth, an increasing number of large-capacity buildings are being built to meet the growing residence demands in many major cities globally, such as Sydney, Hong Kong, London, etc. These modern buildings usually have complex architectural layouts, high-density occupancy settings, which are often filled with a variety of flammable materials and items (i.e., electrical devices, flammable cladding panels etc.). For such reasons, in case of fire accidents, occupants of these buildings are likely to suffer from an extended evacuation time. Moreover, in some extreme cases, occupants may have to escape through a smoke-filled environment. Thus, having well-planned evacuation strategies and fire safety systems in place is critical for upholding life safety. Over the last few decades, due to the rapid development in computing power and modelling techniques, various numerical simulation models have been developed and applied to investigate the building evacuation dynamics under fire emergencies. Most of these numerical models can provide a series of estimations regarding building evacuation performance, such as predicting building evacuation time, visualising evacuation dynamics, identifying high-density areas within the building etc. Nevertheless, the behavioural variations of evacuees are usually overlooked in a significant proportion of such simulations. Noticeably, evacuees frequently adjust their egress behaviours based on their internal psychological state (i.e., the variation of stress) and external stimulus from their surrounding environments (i.e., dynamical fire effluents, such as high-temperature smoke). Evidence suggests that evacuees are likely to shift from a low-stress state to a high-stress state and increase their moving speed when escaping from a high-temperature and smoke-filled environment. Besides, competitive behaviours can even be triggered under certain extremely stressful conditions, which can cause clogging at exits or even stampede accidents. Without considering such behavioural aspects of evacuees, the predicted evacuation performance might be misinterpreted based on unreliable results; thereby, misleading building fire safety designs and emergency precautions. Therefore, to achieve a more realistic simulation of building fire evacuation processes, this research aims to advance in modelling of human dynamical behaviour responses of each evacuee and integrating it into building fire evacuation analysis. A dynamical egress behaviour-based evacuation model that considering the evacuee’s competitive/cooperative egress movements and their psychological stress variation is developed. Furthermore, a fire hazard-integrated evacuation simulation framework is established by coupling with the fire dynamics simulator (i.e., FDS). By means of tracking dynamical interactions between evacuees and the evolutionary fire dynamics within the building space, evacuees’ local fire risks and stress levels under the impacts of locally encountered fire hazards (i.e., radiation, temperature, toxic gas, and visibility) can be effectively quantified. In this study, the developed simulation tool can provide a further in-depth building fire safety assessment. Thus, it contributes to performance-based fire safety engineering in designs and real applications, including reducing budgets and risks of participating in evacuation drills, supporting emergency evacuation strategy planning, mitigating fire risks by identifying risk-prone areas associated with building fire circumstances (e.g., putting preventative measures in place beforehand to intervene or mitigate safety risks, such as mass panic, stampede, stress evoked behaviours)

    Evidence on public health interventions to prevent the negative health effects of climate change

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    Suggested Citation: Anderson H, Brown C, Cameron LL, Christenson M, Conlon KC, Dorevitch S, Dumas J, Eidson M, Ferguson A, Grossman E, Hanson A, Hess JJ, Hoppe B, Horton J, Jagger M, Krueger S, Largo TW, Losurdo GM, Mack SR, Moran C, Mutnansky C, Raab K, Saha S, Schramm PJ, Shipp-Hilts A, Smith SJ, Thelen M, Thie L, Walker R. BRACE Midwest and Southeast Community of Practice. 2017. Climate and Health Intervention Assessment: Evidence on Public Health Interventions to Prevent the Negative Health Effects of Climate Change. Climate and Health Technical Report Series. Climate and Health Program, Centers for Disease Control and Prevention.CS263538-AClimateAndHealthInterventionAssessment_508.pdf2017CDC cooperative agreement awards funded under CDC-RFA-EH13-1305 and CDC-RFA-EH12-1202961

    Developing novel optimization and machine learning frameworks to improve and assess the safety of workplaces

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    This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model\u27s robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to public safety. The second chapter proposes an innovative mathematical model which captures several essential features (e.g., the capacity of the facility and individual choices, heterogeneity of individual behavioral and choice sets, restriction on choice sets depending on the location of the shooter and facility orientation, and many others) which are essential for appropriately characterizing and analyzing the response strategy for civilians under an AS exposed environment. We demonstrate the applicability of the proposed model by implementing the effectiveness of the RUN.HIDE.FIGHT.® (RHF) program in an academic environment. Given most of the past incidents took place in built environments (e.g., educational and commercial buildings), there is an urgent need to methodologically assess the safety of the buildings under an active shooter situation. Finally, the third chapter aims to bridge this knowledge gap by developing a learning technique that can be used to model the behavior of the shooter and the trapped civilians in an active shooter incident. Understanding how the civilians responded to different simulated environments, a number of actions could have been undertaken to bolster the safety measures of a given facility. Finally, this study provides a customized decision-making tool that adopts a tailored maximum entropy inverse reinforcement learning algorithm and utilizes safety measurement metrics, such as the percentage of civilians who can hide/exit in/from the system, to assess a workplace\u27s safety under an active shooter incident

    2nd Edition of Health Emergency and Disaster Risk Management (Health-EDRM)

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    Disasters such as earthquakes, cyclones, floods, heat waves, nuclear accidents, and large-scale pollution incidents take lives and incur major health problems. The majority of large-scale disasters affect the most vulnerable populations, which often comprise extreme ages, remote living areas, and endemic poverty, as well as people with low literacy. Health emergency and disaster risk management (Health-EDRM) refers to the systematic analysis and management of health risks surrounding emergencies and disasters, and plays an important role in reducing the hazards and vulnerability along with extending preparedness, responses, and recovery measures. This concept encompasses risk analyses and interventions, such as accessible early warning systems, the timely deployment of relief workers, and the provision of suitable drugs and medical equipment to decrease the impact of disasters on people before, during, and after an event (or events). Currently, there is a major gap in the scientific literature regarding Health-EDRM to facilitate major global policies and initiatives for disaster risk reduction worldwide

    Velocity-Space Reasoning for Interactive Simulation of Dynamic Crowd Behaviors

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    The problem of simulating a large number of independent entities, interacting with each other and moving through a shared space, has received considerable attention in computer graphics, biomechanics, psychology, robotics, architectural design, and pedestrian dynamics. One of the major challenges is to simulate the dynamic nature, variety, and subtle aspects of real-world crowd motions. Furthermore, many applications require the capabilities to simulate these movements and behaviors at interactive rates. In this thesis, we present interactive methods for computing trajectory-level behaviors that capture various aspects of human crowds. At a microscopic level, we address the problem of modeling the local interactions. First, we simulate dynamic patterns of crowd behaviors using Attribution theory and General Adaptation Syndrome theory from psychology. Our model accounts for permanent, stable disposition and the dynamic nature of human behaviors that change in response to the situation. Second, we model physics-based interactions in dense crowds by combining velocity-based collision avoidance algorithms with external forces. Our approach is capable of modeling both physical forces and interactions between agents and obstacles, while also allowing the agents to anticipate and avoid upcoming collisions during local navigation. We also address the problem at macroscopic level by modeling high-level aspects of human crowd behaviors. We present an automated scheme for learning and predicting individual behaviors from real-world crowd trajectories. Our approach is based on Bayesian learning algorithms combined with a velocity-based local collision avoidance model. We further extend our method to learn time-varying trajectory behavior patterns from pedestrian trajectories. These behavior patterns can be combined with local navigation algorithms to generate crowd behaviors that are similar to those observed in real-world videos. We highlight their performance for pedestrian navigation, architectural design and generating dynamic behaviors for virtual environments.Doctor of Philosoph

    Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

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    This book promotes occupants as a focal point for the design process
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