Emergency Evacuation Strategies Research Articles

A real-time intelligent monitoring method for indoor evacuee distribution based on deep learning and spatial division

Providing real-time and detailed indoor evacuee distribution information is of great significance for the on-site formulating and dynamic adjustment of indoor emergency evacuation strategies. However, due to limitations in hardware costs and privacy protection, existing video surveillance and other evacuee counting methods cannot achieve real-time and accurate monitoring of the number and distribution of evacuees in all indoor areas. To solve the problem, using video data from an indoor surveillance system as a data source, an intelligent monitoring method for indoor pedestrian real-time distribution based on deep learning and spatial division is proposed. Firstly, the whole indoor area is divided many subareas according to the layout of indoor space and the size of spatial units. Then, deep learning detection and tracking algorithms are used to detect and track evacuees to achieve the number of evacuees at the boundary of each subarea and their movement directions; Finally, the numbers of evacuees entering and leaving each subarea are counted to obtain the spatial distribution information of evacuees. The experimental results show that within an appropriate monitoring distance, the proposed method achieves an average F1 score of 91.85% for evacuee counting at the boundaries of each subarea, with a processing speed of 22 FPS. More comprehensive and accurate real-time monitoring of evacuee distribution in the entire indoor space can be achieved, including no covered areas of cameras. It not only helps to formulate on-site emergency evacuation in case of a fire, but also enhances the daily operation and management capabilities of buildings.

Integrated assessment of bioaerosol dispersion patterns and infection risk in a typical urban environment: Implications for urban biosecurity management

Exposure to bioaerosols in high-density urban environments will pose a severe threat to human life and health and present significant challenges to the sustainability and resilience of cities. In this study, the aerodynamic dispersion patterns of bioaerosols at two release locations (open and dense areas) under both thermal conditions in Zhongguancun, Beijing, are investigated. By coupling a dose-response model and an improved cellular automaton, the infection risk within exposed populations was assessed, and emergency evacuation strategies for distinct populations in high-risk areas were devised. This study reveals that bioaerosol distribution is notably influenced by factors such as thermal conditions, release locations and pedestrian height. Bioaerosol concentration above the release source decreases with increasing pedestrian height at two release locations. Under the same exposure time, the infection probability of different groups in this area declines with pedestrian height increases, with adult males having the highest and elderly females having the lowest probability. Thermal conditions and building layout near the release source were second only to exposure time in influencing infection probability and evacuation path. Proximity to the release source indicates a high infection probability but a short evacuation distance to safe areas, while downstream areas exhibit lower infection probability but require longer evacuation distances. The layout of buildings near the release source has the most significant effect on evacuation time. Evacuation for high-risk populations should be prioritized upstream or either side of the mainstream. This study aims to mitigate potential biological threats, address challenges in enhancing urban biosecurity management, and enable sustainable urban development.

Analysis of Emergency Protection and Evacuation Strategies for Disabled People in the Noto Earthquake in Japan

This article takes the Noto earthquake in Japan as the background, studies the emergency protection and evacuation strategies for disabled people during earthquakes, analyzes the characteristics of the Noto earthquake, including the basic information such as the time, location, and magnitude of the disaster, as well as the particularities of the disabled group in terms of physical, psychological, and economic aspects. The article discusses the existing problems, such as incomplete infrastructure, poor information transmission, and insufficient social support, and proposes optimization countermeasures, including improving infrastructure, strengthening information transmission, and increasing social support. This article aims to provide useful references and guidance for the emergency protection and evacuation of disabled people in earthquakes.

Damage mitigation method studies through simulation modeling of chemical accidents

AbstractIn the chemical industry, when a fire occurs, a significant amount of energy is generated due to combustion, impacting other facilities within the plant and potentially leading to severe consequences through a domino effect. For decades, thermal radiation caused by flames has been calculated and predicted through simplified fire modeling. However, with advancements in computing technology, numerical model‐based calculations have greatly improved, allowing for a more realistic implementation that considers actual phenomena. In this study, accident data and 3D modeling information were utilized to conduct fire modeling and simulation based on actual incidents in chemical plants. Through the analysis of simulation results, the initial emergency evacuation distance was provided to minimize the damage caused by thermal radiation, and the final evacuation distance was presented using the probit function. In addition, the study evaluated the impact of generated thermal radiation and overpressure on structures and equipment, providing evidence regarding the potential for secondary incidents. Moreover, the research revealed that the impact of thermal radiation and overpressure decreases due to obstacles, offering insights into the selection of emergency evacuation routes. This study can contribute to supporting effective emergency evacuation strategies in chemical facilities.

Risk perception and travel behavior under short-lead evacuation: Post disaster analysis of 2020 Beirut Port Explosion

Human-made disasters continue to be the most dangerous of all types of disasters due to their suddenness and unpredictability. This calls for a thorough examination of people’s evacuation behavior due to its impact on the evacuation procedure and its significance for evacuation planning. This study aims to provide a comprehensive understanding of the decision-making process of residents in case a human-made disaster occurs with an application to Beirut, Lebanon. This study is essential for pre-disaster planning, which mitigates potential damage from such disasters. Using structural equation modeling (SEM), the current study uses the Protective Action Decision Model (PADM) framework to explain intention toward evacuation behavior before a human-made disaster in three situations: being at home with all family members, having absent family members, and being at work or university when the event occurs. The findings of this study show that the PADM framework is relevant to explaining evacuation behavior intentions prior to a human-made disaster incident. Results indicate that knowledge perception does not trigger the intended behavior of evacuating immediately in the studied situations. Besides, the results indicate low confidence in the government’s emergency plans and the unreliability of the official government warnings about human-made hazards. Overall, the findings of this study may contribute to a better understanding of evacuation behavior from disasters with less lead warning time. Besides, they may aid the Disaster Risk Management unit of Lebanon in developing emergency evacuation strategies that: understand the public’s evacuation behavior; customize city-specific evacuation logistics; optimize the dissemination of evacuation information.

Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling

As different types of hazards, including natural and man-made, can occur simultaneously, to implement an integrated and holistic risk management, a multi-hazard perspective on disaster risk management, including preparedness and planning, must be taken for a safer and more resilient society. Considering the emerging challenges that the COVID-19 pandemic has been introducing to regular hospital operations, there is a need to adapt emergency plans with the changing conditions, as well. Evacuation of patients with different mobility disabilities is a complicated process that needs planning, training, and efficient decision-making. These protocols need to be revisited for multi-hazard scenarios such as an ongoing disease outbreak during which additional infection control protocols might be in place to prevent transmission. Computational models can provide insights on optimal emergency evacuation strategies, such as the location of isolation units or alternative evacuation prioritization strategies. This study introduces a non-ICU patient classification framework developed based on available patient mobility data. An agent-based model was developed to simulate the evacuation of the emergency department at the Johns Hopkins Hospital during the COVID-19 pandemic due to a fire emergency. The results show a larger nursing team can reduce the median and upper bound of the 95% confidence interval of the evacuation time by 36% and 33%, respectively. A dedicated exit door for COVID-19 patients is relatively less effective in reducing the median time, while it can reduce the upper bound by more than 50%.

Fire emergency evacuation model of light rail station based on BIM technology

Urban light rail undertakes the mission of escorting passengers to arrive safely. Once a fire accident occurs, it will inevitably have a great negative impact. Therefore, how to effectively carry out emergency evacuation tasks in fire accidents has become one of the problems facing the development of urban rail transit. Based on this, this article first introduces the relevant characteristics of BIM technology and the corresponding advantages, and further its use in urban light rail stations. The application advantages of emergency evacuation strategies are briefly analyzed; then the requirements for the construction of BIM emergency evacuation models are studied; finally, through field investigations, a specific urban rail transit light rail station is taken as an example to construct a BIM building information model for this light rail station. Applying BIM technology to fire protection work in light rail stations, it is expected to improve the efficiency of emergency evacuation of passengers in light rail stations.

Study on the exit-selecting behavior in underground indoor space with fire using a virtual experiment

The fires in underground indoor space often result in limited field of vision, thus revealing the characteristic of exit-selecting behavior under the influence of fire and limited view is the basis for optimizing exit layout and emergency evacuation strategies for the underground spaces. In this study, an interactive virtual environment is presented and using which an evacuation experiment is carried out to test human exit-selecting behavior in fire scenario. The results show that when the view is limited, more exits and arranging the exits evenly are helpful to shorten the time of seeking the available exits. If there is congestion in front, as long as the information on the exits is sufficient, some people (more than 20%) will re-select the other exits. Furthermore, people prefer to spend more movement time reducing queuing time. When the dynamic temperature field is used to show the participants a spreading fire, the virtual fire plays a certain threat, so most of subjects (96.7%) prefer the exit away from the fire source. Moreover, if there are other pedestrians, the evacuation decision-making process of personnel is the game process among selecting the fastest path, keeping away from the exits near the fire source and avoiding high temperature field. The results also show that individual age has an obvious correlation with the participants’ ability of escaping in the virtual evacuation environment. Specifically, the ability tends to become weaker as the age increased. This study will be useful to understand the human exit-selecting behavior and movement characteristics in the influence of fire and limited view by using a cheap and safe method.

Factors Affecting Adoption of Emergency Evacuation Strategies in High-Rise Office Buildings

This study examined the evacuation strategies employed in high-rise buildings as well as determined factors influencing decision making in employing evacuation strategies in four selected high-rise buildings in Kuala Lumpur, Malaysia. Through a case study evaluation involving interviews with facility/building managers, walk through observations of the case studies and analyses of evacuation exercise reports, the study found that total evacuation was the most practiced evacuation strategy with occasional phased evacuation at one of the case studies. The study identified reliable emergency response, building characteristics/fire safety features, and evacuation exercise as the most important factors influencing decision making in employing evacuation strategies. Occupant characteristics was found to be the least important factor. The argument is that, efficient emergency response, passive and active fire safety systems, and evacuation exercise routines mean that challenges posed by the characteristics of occupants could be overcome. This paper gives new insights on factors influencing decision making in employing suitable evacuation strategies. This would benefit stakeholders e.g. building owners, facility managers, health and safety managers when drafting business continuity plans.

Emergency Evacuation Simulation and Optimization for a Complex Rail Transit Station: A Perspective of Promoting Transportation Safety

Rail transit stations with multifloor structures have been built in many cities to intensively utilize land resources and facilitate lives of community. However, being overcrowded with passengers results in high risks during daily operation. In response, this study conducted an emergency evacuation simulation and optimization in the three-dimensional (3D) space of “complex rail transit stations” (CRTSs). The aim of the paper is to provide a methodology to determine effective emergency evacuation strategies for CRTSs. The Lianglukou Rail Transit Station in Chongqing, China, was used as a case study and the AnyLogic simulation platform employed for simulating emergency evacuations. An emergency evacuation theoretical framework was established. The emergency evacuation strategies, including evacuation routes and evacuation times, were determined based on the theoretical demonstration. Simulation and optimization of emergency evacuation in the Lianglukou station were conducted. Accordingly, four main simulation results were obtained: (1) Escalators/stairs and turnstiles are key facilities in the evacuation; (2) Effective guidance for the evacuation is necessary in the public space of the station; (3) Passenger aggregation nodes should be guided for balanced evacuation; (4) Removing metal barriers is a useful evacuation optimization measure. The proposed research method and framework can be used by other CRTSs in the establishment of emergency evacuation strategies and effective optimization strategies to promote safety of transportation system. The research findings are beneficial to passengers in helping them provide valuable emergency evacuation guidance.

CFD code-to-code benchmarking on simulation of fire and smoke propagation for nuclear applications

Abstract The nuclear industry has seen an increased use of Computational Fluid Dynamics (CFD) technology as a high-fidelity tool for design-basis and beyond design-basis accident simulations. For fire hazards, detailed experimental investigation in a realistic environment is often impractical. Therefore, the use of an advanced 3D CFD modeling approach to predict smoke and fire propagation has risen, especially for nuclear power plant fire modeling applications. CFD simulations can include major contributing and mitigating mechanisms regarding the fire propagation and its consequences, in fine detail and under realistic geometry conditions. In addition, accurate simulations of unsteady 3D fire and smoke spread, in conjunction with evacuation analyses, can be used to establish effective emergency evacuation strategies. In the present study, the fire and smoke propagation in a main control room was modelled using Reynolds-Averaged Navier-Stokes Equation based twoequation turbulence models and a Large Eddy Simulation model implemented in a commercial CFD code, Siemens STAR-CCM+. The predictions from STAR-CCM+ were compared with those obtained in a previous study using the NIST Fire Dynamic Simulator (FDS) code. Although higher temperature was predicted at the fire source by STAR-CCM+, the current study predicted trends similar to the FDS results with some minor differences at the operator location. A follow-up study is currently underway to investigate the factors affecting the differences, including further assessment of codes against available benchmark experiments.

Mobile Fire Evacuation System for Large Public Buildings Based on Artificial Intelligence and IoT

The complexity and variability of the internal environment of public buildings prompt to think about how to protect people in the fire and quickly reach the safe area. With the help of the Internet of Things, firefighting facilities, such as fire hydrants, fire extinguishers, safety evacuation signs, fire sprinklers, fire pumps, smoke, temperature, and fire doors in buildings can be dynamically monitored and controlled. In addition, based on the relevant fire emergency evacuation strategies and ideas at home and abroad, the artificial intelligence technology is used to construct an efficient and intelligent dynamic evacuation path solving model, and an intelligent mobile terminal fire evacuation system was built for large public buildings based on artificial intelligence technology. When a fire breaks out, the system can help guide people to evacuate from the building real-time and reach the safe exit quickly, so as to reduce casualties and economic losses.

A Risk Assessment Approach for Evaluating the Impact of Toxic Contaminants Released Indoors by Considering Various Emergency Ventilation and Evacuation Strategies.

The release of toxic airborne contaminants resulting from terrorist attacks on buildings can lead to disastrous consequences. To evaluate and reduce the effects of these emergencies, various methods and models have been developed in the past few years. Such work has provided effective tools for the building management system to do risk assessment of the contaminated areas. Although risk analysis methods to describe the contaminant dispersion scenarios made significant progress, these approaches did not generally consider the releasing scenario occurring in the ventilation system and the effect of human behavior during the developing process of an emergency event. Emergency strategies chosen by the decisionmaker are not always associated with the early-warning system, such as the sensor monitoring network and the source identification system inside the building. This study aims to provide a risk assessment model considering both the variation of contaminant concentration and occupant distribution after the release of toxic agents to obtain the exposure risk for people indoors. The contaminant dispersion is simulated using computational fluid dynamics. The evacuation process for people is modeled using Pathfinder, and the exposure risk for occupants under various emergency strategies is calculated using the efficiency factor of the contaminant source. The results of the exposure risk for 40 basic cases are discussed, and the optimal ventilation mode for these specific cases is recommended. Next, the impact of the variation of human behavior, contaminant detection time needed by sensors, and source identification time needed by inverse modeling on the exposure risk for people indoor is studied. The uncertainty and reproducibility of the numerical simulationsare emphatically discussed in the Supporting Information.

Evacuation strategy of emergent event in metro station based on the ELECTRE method

Metro station is crowded during rush hours and the structure is usually complex. Hence, the determination of an optimal evacuation strategy is the key for improving the evacuation process efficiency when an emergency event happens. A group decision-making model based on triangular intuitionistic fuzzy numbers (TIFNs) theory is developed for selecting an evacuation strategy in metro station. In this model, a group of experts elicit their preferences about the different evacuation strategies using TIFNs for multiple criteria. The Elimination Et Choice Translation Reality (ELECTRE) method is used to sort the different emergency evacuation strategies proposed for the metro station, and it should be adapted to manage TIFNs above all. The TIFN-ELECTRE model is proposed and applied to select the best emergency evacuation strategy of metro station. Finally, a numerical example with respect to the emergency evacuation strategy of Wuhan Guanggu Square station is carried out.

Instrumental evaluation of upper limb movements in subjects after cervical spinal cord injury

Most of the existing research on emergency evacuation strategies focus on city evacuation planning that highly depends on the use of vehicles or evacuation from buildings. However, for large areas with limited use of vehicles such as theme parks, evacuation of pedestrians and emergent events must be tracked for safety reasons. As hazards may cause certain damages to services, networks with disaster resilience are needed to achieve mission-critical operations such as search and rescue. In this paper, we develop a method for tracking pedestrians and emergent events during disasters by opportunistic ad hoc communication. In our network model, smart-phones of pedestrians store and carry messages to a limited number of mobile sinks. Mobile sinks are responsible for communicating with smart-phones and reaching the emergent events effectively. Since the positioning of the mobile sinks has a direct impact to the network performance, we propose physical force based (PF), grid allocation based (GA) and road allocation based (RA) approaches for sink placement and mobility. The proposed approaches are analyzed through extensive network simulations using real theme park maps and a human mobility model for disaster scenarios. The simulation results show that the proposed approaches achieve significantly better network coverage and higher rescue success without producing increased communication overhead compared to two random mobile sink movement models.

Tracking pedestrians and emergent events in disaster areas

Most of the existing research on emergency evacuation strategies focus on city evacuation planning that highly depends on the use of vehicles or evacuation from buildings. However, for large areas with limited use of vehicles such as theme parks, evacuation of pedestrians and emergent events must be tracked for safety reasons. As hazards may cause certain damages to services, networks with disaster resilience are needed to achieve mission-critical operations such as search and rescue. In this paper, we develop a method for tracking pedestrians and emergent events during disasters by opportunistic ad hoc communication. In our network model, smart-phones of pedestrians store and carry messages to a limited number of mobile sinks. Mobile sinks are responsible for communicating with smart-phones and reaching the emergent events effectively. Since the positioning of the mobile sinks has a direct impact to the network performance, we propose physical force based (PF), grid allocation based (GA) and road allocation based (RA) approaches for sink placement and mobility. The proposed approaches are analyzed through extensive network simulations using real theme park maps and a human mobility model for disaster scenarios. The simulation results show that the proposed approaches achieve significantly better network coverage and higher rescue success without producing increased communication overhead compared to two random mobile sink movement models.

Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty

Nuclear power accidents are one of the most dangerous disasters posing a lethal threat to human health and have detrimental effects lasting for decades. Therefore, emergency evacuation is important to minimize injuries and prevent lethal consequences resulting from a nuclear power accident. An inexact fuzzy stochastic chance constrained programming (IFSCCP) method is developed to address various uncertainties in evacuation management problems. It integrates the interval-parameter programming (IPP) and fuzzy stochastic chance constrained programming (FSCCP) methods into a general framework, in which the IPP method addresses the uncertainties presented as intervals defined by crisp lower and upper bounds, and the FCCP treat the dual-uncertainties expressed as fuzzy random variables. The measures of possibility and necessity were employed to convert the fuzzy random variables into crisp values to reflect the decision maker’s pessimistic and optimistic preferences. The IFSCCP model is applied to support nuclear emergency evacuation management in the Qinshan Nuclear Power Site, which is one of the largest nuclear plants in China. The results pro-vide stable intervals for the objective function and decision variables with different fuzzy and probability confidence levels regarding the local residents’ distribution. Nine scenarios are analyzed to reflect the impacts of the imprecision (fuzziness and randomness) associated with the size of the population in a plume emergency planning zone. The results are valuable for supporting local decision makers to generate effective emergency evacuation strategies.

Possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An Australian case study

This paper aims to develop a capacitated vehicle routing solution to evacuate short-notice evacuees with time windows and disruption risks under uncertainties during a bushfire. A heuristic solution technique is applied to solve the triangular possibilistic model to optimise emergency delivery service. The effectiveness of the proposed algorithm is evaluated by comparing it with a designed genetic algorithm on sets of 20 numerical examples. The model is then applied to the real case study of 2009 Black Saturday bushfires in Victoria, Australia. The results show that it is possible to transfer the last-minute evacuees during the Black Saturday bushfires under the hard time window constraint. Network disruptions however have impact on resource utilisation. The modelling outputs will be useful in the development of emergency plans and evacuation strategies to enhance rapid response to last-minute evacuation in a bushfire emergency.

A study on the fire propagation characteristics in large-opening multi-level limestone mines in Korea

Recently, deployment of large-capacity diesel haulage trucks is rapidly increasing in the domestic large-opening limestone mines due to the high level of operational flexibility. However, in multi-level mines, this trend has raised the serious concern for the risk of diesel vehicle fire. Fire in a deep multi-level mine site is harder to control and likely to develop into disastrous results. Developing the emergency evacuation strategies and controlling the fire should presuppose a knowledge of the fire behavior in mines. This paper aims at studying the behavior of a 25-ton diesel truck fire showing approximately the maximum heat release rate of 30 MW by computational fluid dynamic analysis. Spatial and temporal distributions of the hot air stream, smoke layer, and CO gas layer in multi-level mine sites are evaluated, and the effects of fan operation are scrutinized to find the appropriate fan location and operating mode. In addition, several other aspects of the fire development such as backlayering and throttling effects are also studied. The ultimate goal is to provide fundamental information about fire propagating characteristics in multi-level large-opening underground limestone mines for developing the mine fire evacuation plan and protect the workers’ safety.

Analysis of Kinematic Waves Arising in Diverging Traffic Flow Models

Diverging junctions are important network bottlenecks, and a better understanding of diverging traffic dynamics has both theoretical and practical implications. In this paper, we present a new framework for constructing kinematic wave solutions to the Riemann problem of kinematic wave models of diverging traffic with jump initial conditions. Within this framework, the function space of weak solutions is enlarged to include interior states that occur with stationary discontinuities, and various discrete flux functions in cell transmission models of diverging traffic are used as entropy conditions to pick out unique physical solutions. Then in demand-supply space, we prove that the Riemann problem can be uniquely solved for existing discrete flux functions, in the sense that stationary states and, therefore, shock or rarefaction waves on all links, can be uniquely determined under given initial conditions. We show that the two diverge models by Lebacque and Daganzo are asymptotically equivalent. We also prove that the supply proportional and priority-based diverge models are locally optimal evacuation strategies. With numerical examples, we demonstrate the validity of the analytical solutions of interior states, stationary states, and corresponding kinematic waves. This study presents a unified framework for analyzing traffic dynamics arising at diverging junctions and could be helpful for developing emergency evacuation strategies.