Multi-Scale Evacuation Models To Support Emergency And Disaster Response

Evacuation is a short-term measure to mitigate human injuries and losses by temporarily relocation of exposed population before, during, or after disasters. With the increasing growth of population and cities, buildings and urban areas are over-populated which brings about safety issues when there is a need for emergency evacuation. In disaster studies, simulation is widely used to explore how natural hazards might evolve in the future, and how societies might respond to these events. Accordingly, evacuation simulation is a potentially helpful tool for emergency responders and policy makers to evaluate the required time for evacuation and the estimated number and distribution of casualties under a disaster scenario. The healthcare system is an essential subsystem of communities which ensures the health and well-being of their residents. Hence, the resilience of the healthcare system plays an essential role in the resilience of the whole community. In disasters, patient mobility is a major challenge for healthcare systems to overcome. This is where the scientific society enters with modeling and simulation techniques to help decision-makers. Hospital evacuation simulation considering patients with different mobility characteristics, needs, and interactions, demands a microscopic modeling approach, like Agent-Based Modeling (ABM). However, as the system increases in size, the models become highly complex and intractable. Large-scale complex ABMs can be reduced by reformulating the micro-scale model of agents by a meso-scale model of population densities and partial differential equations, or a macro-scale model of population stocks and ordinary differential equations. However, reducing the size and fidelity of microscopic models to meso- or macro-scale models implies certain drawbacks. This dissertation contributes to the improvement of large-scale agent-based evacuation simulation and multi-scale hospital evacuation models. For large-scale agent-based models, application of bug navigation algorithms, popular in the field of robotics, is evaluated to improve the efficiency of such models. A candidate bug algorithm is proposed based on a performance evaluation framework, and its applicability and practicability are demonstrated by a real-world example. For hospital evacuation simulation, crowd evacuation considering people with different physical and mobility characteristics is modeled on three different scales: microscopic (ABM), mesoscopic (fluid dynamics model), and macroscopic (system dynamics model). Similar to the well-known Predator-Prey model, the results of this study show the extent to which macroscopic and mesoscopic models can produce global behaviors emerging from agents’ interactions in ABMs. To evaluate the performance of these multi-scale models, the evacuation of the emergency department at Johns Hopkins University is simulated, and the outputs and performance of the models are compared in terms of implementation complexity, required input data, provided output data, and computation time. It is concluded that the microscopic agent-based model is recommended to hospital emergency planners for long-term use such as evaluating different emergency scenarios and effectiveness of different evacuation plans. On the other hand, the macroscopic system dynamics model is best to be used as a simple tool (like an app) for rapid situation assessment and decision making in case of imminent events. The fluid dynamics model is found to be suitable only for studying crowd dynamics in medium to high densities, but it does not offer any competency as an evacuation simulation tool

An Integrated Agent-Based Microsimulation Model for Hurricane Evacuation in New Orleans

Mass evacuation of urban areas due to hurricanes is a critical problem that requires extensive basic and applied research. Knowing the accurate evacuation time needed for the entire region in advance such that the evacuation order can be issued on a timely basis is crucial for the officials. Microsimulation modeling, which focuses on the characteristics of individual motorists and travel behavior, has been used widely in traffic simulation as it can lead to the most accurate result. However, because detailed driver response modeling and path processing must be incorporated, vehicle-based microscopic models have always been used only to simulate small to medium sized urban areas. Few studies have attempted to address problems associated with mass evacuations using vehicle-based microsimulation at a regional scale. This study develops an integrated two-level approach by separating the entire road network of the study area into two components, highways (i.e., interstate highways and causeways) and local roads. A vehicle-based microsimulation model was used to simulate the highway part of the road traffic, whereas the local part of the road traffic simulation utilized an agent-based model. The integrated microsimulation model was used to simulate hurricane evacuation in New Orleans. Validation results confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. Sufficient evacuation time is a premise to protect people’s life safety when an area is threatened by a deadly disaster. To decrease the network clearance time, this study also examined the effectiveness of three evacuation strategies for disaster evacuation, including a) simultaneous evacuation strategy, b) staged evacuation strategy based on spatial vulnerabilities, and c) staged evacuation strategy based on social vulnerabilities. The simulation results showed that both staged evacuation strategies can decrease the network clearance time over the simultaneous evacuation strategy. Specifically, the spatial vulnerability-based staged evacuation strategy can decrease the overall network clearance time by about four hours, while the social vulnerability-based staged evacuation strategy can decrease the network clearance time by about 2.5 hours

Enhancing Evacuation Planning in Public Buildings: Optimising Egress Location and Protection

Effective evacuation strategies are crucial for ensuring the safety of individuals during emergencies and disasters. Despite significant progress in evacuation planning, the intricate dynamics of disaster scenarios and uncertainties inherent in such situations need to be better incorporated in planning egress locations to enhance safety in buildings. This work focuses on strategically locating egress points within public buildings, acknowledging their pivotal role in facilitating secure evacuations. Optimising egress points improves evacuation efficiency and minimises associated risks, significantly improving evacuation. This research introduces an innovative approach that integrates optimisation models, addresses decision-making complexities, explores practical applications, and considers potential attack scenarios. The study explores evacuation dynamics across diverse scenarios, elevating preparedness, and safety protocols to protect public assets and lives. Developing mixedinteger programming models establishes a foundation for optimising egress locations. MCDM is then employed, leveraging the F-AHP to address uncertainties in egress selection. Practicality is realised through integrating Revit and AnyLogic software, facilitating assessment through BIM and ABM. A stochastic BP model is formulated, addressing both Defender and Attacker perspectives for enhanced egress strategies. This model strategically allocates resources to fortify egresses, ensuring occupant safety during evacuations. Contributions further optimisation approaches, fortification strategies, and progressive enhancements in evacuation planning. These collectively address key challenges and gaps in existing literature, enhancing evacuation efficiency and public safety during emergencies. The research bridges gaps in existing approaches, providing a framework for future investigations into optimising evacuation strategies, enhanced disaster preparation, and further advancements in the field

Developing an agent-based evacuation simulation model based on the study of human behaviour in fire investigation reports

Fire disasters happen every day all over the world. These hazardous events threaten people's lives and force an immediate movement of people wanting to escape from a dangerous area. Evacuation drills are held to encourage people to practise evacuation skills and to ensure they are familiar with the environment. However, these drills cannot accurately represent real emergency situations and, in some cases, people may be injured during practice. Therefore, modelling pedestrian motion and crowd dynamics in evacuation situations has important implications for human safety, building design, and evacuation processes. This thesis focuses on indoor pedestrian evacuation in fire disasters. To understand how humans behave in emergency situations, and to simulate more realistic human behaviour, this thesis studies human behaviour from fire investigation reports, which provide a variety details about the building, fire circumstance, and human behaviour from professional fire investigation teams. A generic agent-based evacuation model is developed based on common human behaviour that indentified in the fire investigation reports studied. A number of human evacuation behaviours are selected and then used to design different types of agents, assigning with various characteristics. In addition, the interactions between various agents and an evacuation timeline are modelled to simulate human behaviour and evacuation phenomena during evacuation. The application developed is validated using three specific real fire cases to evaluate how closely the simulation results reflected reality. The model provides information on the number of casualties, high-risk areas, egress selections, and evacuation time. In addition, changes to the building configuration, number of occupants, and location of fire origin are tested in order to predict potential risk areas, building capacity and evacuation time for different situations. Consequently, the application can be used to inform building designs, evacuation plans, and priority rescue processes

Development of a Dynamical Egress Behavioural Model under Building Fire Emergency

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)

Human factors investigation of the behavioural response to cues of a fire emergency

Safety is a significant priority in the contemporary building environment and a focus for many organisations and businesses. Studies have been conducted to review different factors regarding human behaviour during fire evacuation and to utilize the findings to model improved egress procedures and to train occupants on how to evacuate safely. However, much is still unknown about the processes of perceiving and responding to an emergency when cues from different information sources conflict. For example, when a fire evacuation warning has been issued, but the conditions in the area appear to be fine, some of the building occupants may have uncertainty about the correct action to take. There are several cues to an emergency, and some of these may not lead to optimum behaviour. For example, prior research has shown that, in cases where there has been a prevalence of nuisance alarms such as false alarms, occupants may not take action when a real fire alarm is sounded (Proulx, 2007). Moreover, cues to an emergency are often ambiguous and may not be immediately perceived as a threat. This research was conducted to understand the human responses to cues of an emergency in greater detail. It was based on the Protective Action Decision Model (PADM) (Lindell & Perry, 2012), which outlines the research framework conducted within this PhD. PADM provides a formal model of human behaviour during an emergency. Still, it should be expanded into a more comprehensive method of predicting how people behave in a fire or an evacuation (Kuligowski, 2013). The PADM model identifies several stages in the process of emergency detection and response. The first stage defines several factors that influence awareness of a fire scenario; environmental and social contexts, information sources, warning messages, channel access, and receiver characteristics. This PhD conducted a series of experimental studies to identify the influence of some of these factors on user response to fire alarm cues. The research also compared the use of different research methods, specifically, scenario talk through and virtual reality (VR) simulation, to evaluate user behaviour in response to a fire alarm. Four studies have been conducted: the first extended the talk-through method previously used by Lawson et al. (2013) by adding the influence of social cues to the fire scenario. The second study presented the same fire scenario and influence of social cues as study 1, using VR. The pattern of results was consistent with previous literature in that passive behaviour of others resulted in longer evacuation times for the participants. Thus, these methods can reveal the influence of social behaviour on predicting human responses to an emergency. Study three extended the VR scenario to include other factors from stage one of the PADM model. These factors include the source of information during an emergency, the content of the information, and the recipient's characteristics. Therefore, the source of information, level of details, and information channels were all identified as significant in emergencies such as fire evacuations. Finally, the fourth study was conducted to understand the effects of social cues (passive or active conflict) on an authority figure or siren in the evacuation process. Again, three groups were identified and exposed to three different messages in a virtual environment. Results showed that an authority figure in an active conflict situation showed a significant reduction in the evacuation times. Thus, this thesis will show that understanding behavioural response to fire emergency cues has potential value in predicting human behaviour in a fire emergency

Human factors investigation of the behavioural response to cues of a fire emergency

Safety is a significant priority in the contemporary building environment and a focus for many organisations and businesses. Studies have been conducted to review different factors regarding human behaviour during fire evacuation and to utilize the findings to model improved egress procedures and to train occupants on how to evacuate safely. However, much is still unknown about the processes of perceiving and responding to an emergency when cues from different information sources conflict. For example, when a fire evacuation warning has been issued, but the conditions in the area appear to be fine, some of the building occupants may have uncertainty about the correct action to take. There are several cues to an emergency, and some of these may not lead to optimum behaviour. For example, prior research has shown that, in cases where there has been a prevalence of nuisance alarms such as false alarms, occupants may not take action when a real fire alarm is sounded (Proulx, 2007). Moreover, cues to an emergency are often ambiguous and may not be immediately perceived as a threat. This research was conducted to understand the human responses to cues of an emergency in greater detail. It was based on the Protective Action Decision Model (PADM) (Lindell & Perry, 2012), which outlines the research framework conducted within this PhD. PADM provides a formal model of human behaviour during an emergency. Still, it should be expanded into a more comprehensive method of predicting how people behave in a fire or an evacuation (Kuligowski, 2013). The PADM model identifies several stages in the process of emergency detection and response. The first stage defines several factors that influence awareness of a fire scenario; environmental and social contexts, information sources, warning messages, channel access, and receiver characteristics. This PhD conducted a series of experimental studies to identify the influence of some of these factors on user response to fire alarm cues. The research also compared the use of different research methods, specifically, scenario talk through and virtual reality (VR) simulation, to evaluate user behaviour in response to a fire alarm. Four studies have been conducted: the first extended the talk-through method previously used by Lawson et al. (2013) by adding the influence of social cues to the fire scenario. The second study presented the same fire scenario and influence of social cues as study 1, using VR. The pattern of results was consistent with previous literature in that passive behaviour of others resulted in longer evacuation times for the participants. Thus, these methods can reveal the influence of social behaviour on predicting human responses to an emergency. Study three extended the VR scenario to include other factors from stage one of the PADM model. These factors include the source of information during an emergency, the content of the information, and the recipient's characteristics. Therefore, the source of information, level of details, and information channels were all identified as significant in emergencies such as fire evacuations. Finally, the fourth study was conducted to understand the effects of social cues (passive or active conflict) on an authority figure or siren in the evacuation process. Again, three groups were identified and exposed to three different messages in a virtual environment. Results showed that an authority figure in an active conflict situation showed a significant reduction in the evacuation times. Thus, this thesis will show that understanding behavioural response to fire emergency cues has potential value in predicting human behaviour in a fire emergency

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly

A multi-organisational approach for disaster preparedness and response:the use of optimisation and GIS for facility location, stock pre-positioning, resource allocation and relief distribution

From 1992 to 2012 4.4 billion people were affected by disasters with almost 2 trillion USD in damages and 1.3 million people killed worldwide. The increasing threat of disasters stresses the need to provide solutions for the challenges faced by disaster managers, such as the logistical deployment of resources required to provide relief to victims. The location of emergency facilities, stock prepositioning, evacuation, inventory management, resource allocation, and relief distribution have been identified to directly impact the relief provided to victims during the disaster. Managing appropriately these factors is critical to reduce suffering. Disaster management commonly attracts several organisations working alongside each other and sharing resources to cope with the emergency. Coordinating these agencies is a complex task but there is little research considering multiple organisations, and none actually optimising the number of actors required to avoid shortages and convergence. The aim of the this research is to develop a system for disaster management based on a combination of optimisation techniques and geographical information systems (GIS) to aid multi-organisational decision-making. An integrated decision system was created comprising a cartographic model implemented in GIS to discard floodable facilities, combined with two models focused on optimising the decisions regarding location of emergency facilities, stock prepositioning, the allocation of resources and relief distribution, along with the number of actors required to perform these activities. Three in-depth case studies in Mexico were studied gathering information from different organisations. The cartographic model proved to reduce the risk to select unsuitable facilities. The preparedness and response models showed the capacity to optimise the decisions and the number of organisations required for logistical activities, pointing towards an excess of actors involved in all cases. The system as a whole demonstrated its capacity to provide integrated support for disaster preparedness and response, along with the existence of room for improvement for Mexican organisations in flood management