Actual Evacuation Research Articles

Using human mobility data to detect evacuation patterns in hurricane Ian

ABSTRACT Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters.

The study on mechanical model considering optimal self-adaption in the bottleneck area

It aims to solve the problem that the evacuation state of pedestrians depicted by the traditional social force model in a crowded multiexit scenario has a relatively large difference with the actual state, especially the 'optimal path' considered by the self-driving force is the problem of shortest path, and the multiexit evacuation mode depicted by the 'herd behavior' is the local optimum problem. Through in-depth analysis of actual evacuation data of pedestrians and causes of problem, a new crowd evacuation optimization model is established in order to effectively improve the simulation accuracy of crowd evacuation in a multi-exit environment. The model obtains the direction of motion of pedestrians using a field model, fully considers the factors such as exit distance, distribution of pedestrians and regional crowding degree, makes a global optimization for the self-driving force in the social force model using a centralized and distributed network model, and makes a local optimization for it using an elephant herding algorithm, so as to establish a new evacuation optimization method for optimal self-adaption in the bottleneck area. The performance status is compared between the improved social force model and the new model by experiments, and the key factors that affect the new model are analyzed in an in-depth manner. The results show that the new model can optimize the optimal path choice at the early stage of evacuation and improve the evacuation efficiency of pedestrians at the late stage, so as to ensure relatively even distribution of pedestrians at each exit, and also make the simulated evacuation process be more real; and the improvement in overall evacuation efficiency is greater when the number of pedestrians to be evacuated is larger. Therefore, the new model provides a method to solve the phenomenon of disorder in overall pedestrian evacuation due to excessive crowd density during the process of multi-exit evacuation.

Development of Education and Training Scenarios to Improve the Ability of Fire Safety Managers to Realize Situations and Induce Evacuation in Case of Fire

This study developed an education and training scenario to improve the ability of fire safety managers to recognize situations during a fire and induce evacuation. By reflecting on the characteristics of evacuation facilities and the occupants of multi-occupancy buildings, a scenario similar to that of a real situation was designed through unexpected situations that could occur during actual evacuation induction. Each evacuation facility in the scenario was developed by applying additional methods considering the factors influencing the evacuation in the previous case, referring to Article 4 (1) of the adaptability of evacuation equipment according to the installation location of firefighting objects. The developed scenario was a multi-occupancy building with four basement levels and 50 floors above the ground with a piloti structure and was designed based on the order of evacuation initiation → induction of evacuation → gather at the evacuation site → rescue recipient state report. The details were designed to be applied to practical training by assigning elements that can occur in real situations by causing evacuation using evacuation facilities according to the number of fire floors, assigning situations through various characteristics of occupants, and inserting role conflict elements through unexpected situations. By conducting practical and feasible education for fire safety managers, they can flexibly cope with unexpected situations that may occur in the process of inducing occupants to evacuate in the event of a fire while on duty and improve their skills in using evacuation facilities.

Comparison of kinetic changes during helicopter medical evacuations: civilian versus military flights

BackgroundHelicopter evacuation is crucial for providing medical care to casualties. Previous civilian studies have demonstrated that air transport can enhance survival rates compared with ground transport. However, there has been...

A queuing evacuation model with yielding behavior

A new super queue model with yielding behavior is proposed to model the orderly evacuation process of young pupils in a classroom building. A super queue is composed of a super leader and several simple queues. Based on this, a leader-follower model is proposed to model the orderly behavior between pupils in a super queue, and an exclusive channel model is proposed to model the yielding behavior between super queues. Taking the evacuation of a classroom building as an example, the super queues and yielding behavior are reproduced and are very similar to the actual behavior (both qualitatively and quantitatively). Based on the model, scenarios are simulated to analyze the impacts of several factors on evacuation efficiency. The results show the following: (1) The stop-and-go effect greatly affects the overall evacuation efficiency. Therefore, it is necessary for students to maintain sufficient concentration to shorten their reaction time during the stopping and going process, thereby improving the evacuation efficiency. (2) Different yielding strategies have little impact on the overall evacuation efficiency. Therefore, in an actual evacuation case, flexible yielding strategies can be adopted in order to consider the needs of each class. (3) By optimizing evacuees’ behaviors, such as improving their reaction times, the efficiency of an evacuation with queuing and yielding can become close to that of a disordered evacuation. At the same time, due to its higher safety and easier management process, a queuing and yielding evacuation can be a safe and relatively efficient method for real evacuations.

How Post-Immunization COVID-19 Context Affected Residents’ Evacuation Behavior during Hurricane Ida

Abstract Since the onset of the COVID-19 pandemic, decision-making during disasters fundamentally changed to accommodate the combined risks of hurricanes and infectious diseases. Prior research conducted in 2020 by Collins et al. examined how individuals changed their intended evacuation decision-making during the pandemic or their actual evacuation decisions during Hurricanes Laura and Sally. Hurricane Ida provided further data on evacuation decision-making when vaccinations and masks were widely available. A digital survey was disseminated to individuals affected by Hurricane Ida in 2021. Respondents provided information about their actual evacuation choices and perceptions of public shelters and COVID-19 risks. In comparison with the 2020 hurricane season, more individuals have reduced negative perceptions of hurricane shelters. However, individuals were less likely to utilize public shelters than in the 2020 season, with 11.4% more individuals stating they would definitely or probably avoid using shelters in 2021. Fewer individuals identified that COVID-19 was a primary reason they chose to stay home during Hurricane Ida (19.5% as compared with 86.8% during Hurricanes Laura and Sally). Furthermore, respondents with health risks for severe COVID-19 symptoms were no more likely to evacuate than those respondents who had no health risks. Potentially, as the pandemic progressed and vaccine availability and COVID-19 management improved, COVID-19 has had less impact on evacuation decision-making. The results from this work should guide planners in emergency management and public health in future hurricane seasons and future pandemics or other outbreaks to anticipate behavior changes and properly manage infectious disease threats.

Disparity between evacuation intention and actual evacuation behavior during a heavy rain induced disaster event in Japan: Exploring the role of everyday awareness of disaster preparedness

The purpose of this study was to identify the ex-ante determinants of disaster preparedness in daily life, termed everyday awareness of disaster preparedness, which influence evacuation behavior during heavy rainfall events. A panel survey was conducted among residents of flood- and landslide-prone areas in Ehime Prefecture before and after heavy rainfall. A pre-survey explored a wide range of variables related to participants’ everyday awareness of disaster preparedness prior to heavy rains, followed by a post-survey that examined their actual evacuation behavior when evacuation warnings were issued during subsequent heavy rains. By analyzing the survey data, we identified prior variables related to disaster preparedness that were associated with evacuation choice. The participants had a high level of disaster preparedness, including evacuation intentions, but many of them did not actually evacuate even when evacuation warnings were issued due to heavy rains. Furthermore, only prior contemplation of evacuation criteria (associated with the commitment to evacuate) was directly related to evacuation behavior. Finally, mediation analysis indicated that prior contemplation mediated the relationship between evacuation intentions and actual evacuation. The implications of these findings for education and communications to reduce disaster risk are discussed.

A Scientometric Research on Applications and Advances of Fire Safety Evacuation in Buildings

Fire safety evacuation has been used in numerous different kinds of buildings. This research conducts a scientometric review of fire safety evacuation applications and advances in the buildings to clarify the research trends of fire evacuation in the future and provide guidance for relevant research. A total of 3312 journals and conference proceedings were analyzed through different dimensions. The result proves that evacuation environments concentrate mainly on residential building, commercial building, school, and railway station. The characteristics of the evacuee have been gradually refined in recent years, including children, the elderly, patients, and vulnerable groups. The main experimental approaches of fire safety evacuation are evacuation drills, site records, and VR/AR experiments. The crowd behavior models mainly consist of six types: a cellular automata model, a social force model, a lattice gas model, a game-theoretic model, an animal agent-based model, and a computer agent-based model. The analysis results in the theoretical method are becoming gradually closer to the behavioral characteristics and movement data of the crowd during the actual evacuation with improvements of practical considerations. The study of evacuation drills, disaster rescue, emergencies, and other external environmental factors will become the forefront of future research, and subway stations, airports, high-rise building, and other personnel places will be the focus of the study of crowd evacuation.

Using GIS to Determine Tsunami Shelter Location in South Coast Pangandaran District, Pangandaran Regency, Indonesia

The geographical condition of the Pangandaran Regency affects the coastal area, periodically suffering hydrological hazards. Based on the Regional Disaster Management Agency of Pangandaran, Pangandaran Regency experienced Tsunami with a run-up height of 8 to 15.7 meters in 2006. The Tsunami caused 664 people to die, many to be injured, and caused social environment disruption. Therefore, this research aims to support disaster mitigation by identifying additional shelter locations and generating evacuation routes sequentially. This research uses the network analyst method of Geographic Information System (GIS). The identification of evacuation shelters utilizes several parameters, e.g., tsunami hazard catchment area, land slope levels, land cover, and street network. In addition, the spatial analysis considers average travel time as the impedance to resemble the actual evacuation process. The result indicates that 11 evacuation shelters scattered over the study area were dominantly located in Pangandaran Village. Moreover, this research generates 24 evacuation routes from the incident location to each shelter. The closest shelters accessed 8 minutes from incidents 4 and 18 are shelters 3 and 9, respectively. On the contrary, the farthest shelter is shelter 9, with a maximum impedance of 22 minutes from incidents 14 and 15. Furthermore, the most appropriate shelters are shelters 2 and 8, considering the elevation height of 10 and 7 in the open space.

Evaluation of Hurricane Evacuation Order Plans: Hurricane Florence Case Study

This paper introduces an approach to evaluate the performance of a previously implemented or proposed hurricane evacuation plan that describes where and when official evacuation orders are issued. The approach involves use of the new integrated scenario-based evacuation (ISE) decision support tool to define a best track evacuation plan as a reference point and measure the performance of other plans in relation to that according to their ability to meet multiple stated objectives: minimizing risk to the population, travel time, and time people are away from their homes. Using North Carolina in Hurricane Florence (2018) as a case study, we demonstrate the process by evaluating performance of both the actual set of orders as executed and the orders that would have been recommended if the new ISE decision support tool had been used during the event. All three plans were evaluated for two cases—assuming the hurricane unfolds as it actually did, and if the hurricane had instead evolved like one of 21 other realistic scenarios. Results suggest the actual evacuation was quite good, and the ISE tool could have resulted in improved evacuation performance.

Model considering panic emotion and personality traits for crowd evacuation

Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN (openness, conscientiousness, extroversion, agreeableness, neuroticism) model and SIS (susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.

3D INDOOR-PEDESTRIAN INTERACTION IN EMERGENCIES: A REVIEW OF ACTUAL EVACUATIONS AND SIMULATION MODELS

Abstract. Pedestrian motions and behaviours in evacuations are compactly interrelated indoor environments. According to how pedestrians interact with indoor environments in three-dimensional (3D) space, 3D indoor-pedestrian interaction is defined as five sorts of specific pedestrian motions, i.e., stepping over, crawling, bent-over walking, jumping over and climbing over. However, the occurrence and prevalence of the interaction in actual evacuations have not been identified, and the degree of effectiveness and practicality of the evacuation simulation models regarding the interaction is yet to be evaluated. Understanding the interaction and simulating it realistically bring apparent benefits to support informed policymaking, building safety optimization and emergency relief efforts. Therefore, this article aims to review the 3D indoor-pedestrian interaction in actual evacuations, assess the capabilities and performance of the evacuation simulation models for the interaction, and identify their limitations and challenges for future work. We first demonstrate that the fundamental relationships between the decision-making process of a pedestrian, object distance and object size/shape in 3D space are unclear yet, and the scarcity of experimental investigation for local routes choices and each motion is vast. Subsequently, the comparisons between Social Force models, Cellular Automata models and Agent-based models suggest that these microscopic models can yield the 3D indoor-pedestrian interaction, but the height dimension of 3D space is not explicitly considered during the evacuation modelling. This article is expected to advance the understanding of pedestrian evacuations in 3D space, ultimately improving pedestrian safety in indoor environments.

Hurricanes Laura and Sally: A Case Study of Evacuation Decision-Making in the Age of COVID-19

Abstract This study examines risk perceptions and evacuation planning for those residents affected by Hurricane Laura—the first major hurricane evacuation during the COVID-19 pandemic—and Hurricane Sally, prior to the widespread availability of vaccines. Research on hurricane evacuation behavior and risk perceptions during a pandemic is critical for quantifying the intersect of these compounding threats. Analyses captured how people perceive public shelters and whether evacuation choices changed in light of the pandemic. Many study participants considered themselves vulnerable to COVID-19 (39.4%), and two-thirds believed it would be “very serious” if they or their loved ones contracted COVID-19, but this had no impact on their actual evacuation decision-making. Approximately 75% of the sample stayed at home during Hurricanes Laura or Sally, and, of these, just over 80% indicated that COVID-19 was a somewhat important deciding factor. This reflects the partial role that COVID-19 played in balancing individual and household protective action decision-making during complex disasters, whereas 15.5% wanted to evacuate but waited until it was too late. For those who evacuated to a hotel, many found that staff and guests wore masks and socially distanced in common spaces. Of particular interest is that individuals have a continued negative perception of public shelters’ ability to safeguard against COVID-19 that was coupled with a significant decrease in the number of respondents that would potentially use shelters in 2020 as compared with before the COVID-19 pandemic. These results have informed and will inform future hazard mitigation planning during the current pandemic or future pandemics or infectious disease outbreaks.

Integrating wildfire spread and evacuation times to design safe triggers: Application to two rural communities using PERIL model

The hazards posed by a wildfire increase significantly when it approaches the wildland–urban interface. Evacuation of rural communities is frequently considered by local authorities and residents. In this context, evacuation triggers are locations that when reached by the wildfire indicate it is time to evacuate. Triggers are often arbitrarily defined via identifiable landmarks, do not include a safety factor, and do not account for fire spread or how long the evacuation takes. Ill-designed triggers may not safely inform decision making. It is necessary to create evacuation triggers that take into account both how a fire spreads towards the community, and how a community evacuates. This paper outlines a framework for developing triggers through the coupling of wildfire and evacuation models. We implement the previous theory of Cova et al. (2005) and others on triggers into a tool known as PERIL for generating trigger perimeters around a community, using the fire spread model FARSITE. A safety factor is included to address uncertainties in the wildfire or evacuation calculations. PERIL was applied to two real communities for the Swinley forest community (UK), and Roxborough Park community (USA). These communities were chosen because of previous work studying their actual evacuations. PERIL, which is available in open source, can be applied to inform safer strategies for to protect rural communities threatened by wildfires.

An emergency aircraft evacuation simulation considering passenger overtaking and luggage retrieval

The irrational behaviors of passengers due to panic and anxiety during actual evacuations are challenging to be fully controlled by the crew. These behaviors may lead to the inefficiency of actual evacuation. This research builds a cabin evacuation model considering passengers’ luggage retrieval and overtaking behaviors based on cellular automata to improve the safety of aviation passengers.. The model introduces disaster levels to describe passengers’ anxiety and implements the “faster-is-slower” effect. Two possible overtaking routes are proposed, and we explored the effect of overtaking and retrieving luggage. The result shows that both overtaking and luggage retrieval negatively impact evacuation efficiency, and the impact of baggage retrieving is greater than overtaking. However, overtaking can essentially eliminate the influence of baggage retrieving. The effect of prohibiting irrational behaviors in different cabin compartments is analyzed to simulate crews’ guidance. The results show that the crew's enhanced guidance of the second part is the most effective.

Preliminary Observations and Impact in Japan of the Tsunami Caused by the Tonga Volcanic Eruption on January 15, 2022

The tsunami caused by the Tonga submarine volcanic eruption that occurred at 13:15 Japan Time (JST) on January 15, 2022, exposed a blind spot in Japan’s tsunami monitoring and warning system, which was established in 1952 for local tsunamis and expanded to distant tsunamis after the 1960 Chile tsunami. This paper summarizes how the warning system responded to the unprecedented tsunami, the actual evacuation process, and the damage it caused in Japan. Initially, the tsunami from the volcanic eruption was expected to arrive at approximately midnight with amplitudes of less than 20 cm. However, a series of short waves arrived at approximately 21:00, a few hours earlier than expected. The early arrival of these sea waves coincided with a rapid increase in atmospheric pressure; then, the short-period component was predominant, and the wave height was amplified while forming wave groups. After a 1.2 m tsunami was observed in Amami City in southern Japan at 23:55 JST, the Japan Meteorological Agency issued a tsunami warning/advisory. The tsunami continued, and all advisories were cleared at 14:00 JST on January 16. Information about this tsunami and the response to it are summarized here, including the characteristics and issues of the actual tsunami evacuation situation in each region. There were no casualties, but the issues that emerged included difficulty evacuating on a winter night and traffic congestion due to evacuation by car and under the conditions of the COVID-19 pandemic. In the coastal area, damage to fishing boats and aquaculture facilities was reported due to the flow of the tsunami. In addition, damage to aquaculture facilities, including those producing oysters, scallops, seaweed and other marine products, decreased the supply of marine products, and the economic impact is likely to increase in the future.

Application of Cellular Automata with Improved Dynamic Analysis in Evacuation Management of Sports Events

In this paper, the cellular automaton simulation technology is applied to the evacuation management of sports events, combined with the dynamic analysis method to analyze the collision of people in the evacuation process. At the same time, the cellular automaton model is used to refine the evacuation space to simplify environmental modeling, and the network model is used to determine the individual evacuation path from a macroperspective, simplify modeling, and improve simulation efficiency. In addition, this article simulates the evacuation process of the cellular automaton according to the actual evacuation situation of sports events and constructs the evacuation management system of sports events. Finally, this article evaluates the effect of the model in conjunction with experiments. The experimental results show that the role of the evacuation management system for sports events based on cellular automata proposed in this paper is obvious.

Understanding total evacuation time perception in airplane emergency: A stated preference approach

It is difficult to observe people's actual evacuation behavior in case of a real aircraft accident. For one, luckily, because aircraft incidences have very low probabilities, and for another, because when faced with such extreme situations people often resort to very unexpected behavior. A way to obtain people’s responses to an airplane emergency is then to estimate people’s perceptions of the total evacuation time. In this paper, a stated preference analysis containing eight airplane evacuation attributes was carried out by nine hypothetical aircraft accident scenarios. The experiment adopted a fractional factorial design to reduce the survey size and obtained 192 effective responses. A cumulative odds ordinal logistic regression with proportional odds was performed and results showed the model has a good data fit. The following findings were revealed from this study. First, for the onboard safety briefings, the efficiency of safety broadcasting is better than crew demonstrations and safety cards, but the overall attention from passengers to the cabin safety announcements is still inadequate. Second, in passengers’ perception, the lack of crew guidance would slow the evacuation the most while more available exits reduce the evacuation time primarily. Third, being knowledgeable about and more experienced in flying has a significant effect in reducing people’s perception of evacuation time. Finally, higher self-reported levels in the ability to stay calm are associated with a shorter estimation of the evacuation time. These findings may help optimize the understanding of air passengers’ behavior in emergencies and improve the preparedness for aircraft emergency evacuation.

Tsunami awareness and preparedness in Aotearoa New Zealand: The evolution of community understanding

After catastrophic events such as the 2004 Indian Ocean tsunami and the 2011 Great East Japan earthquake and tsunami there is a clear need for vulnerable countries like Aotearoa New Zealand to get prepared for tsunami. In the last ten years, the New Zealand government initiated major efforts to raise awareness of tsunami risk among coastal residents. This study explores tsunami awareness, preparedness, and evacuation intentions among residents of the East Coast of the North Island in a 2015 survey. The ten chosen locations also participated in a tsunami survey in 2003, with results demonstrating that tsunami awareness rose in the twelve years between the surveys. The 2015 survey also included questions on preparedness and intended action. Even though coastal residents know they live in a tsunami prone area, preparedness is relatively low and high expectations of a formal warning remain, even for a local source tsunami scenario. Furthermore, survey respondents had unrealistic ideas of evacuation procedures. When asked about their evacuation intentions, respondents intended to undertake a number of different actions before evacuating their homes, which could cause significant delays in the evacuation process. Most respondents were also reluctant to evacuate on foot and prefer using their vehicles instead, which could create dangerous traffic congestion. These surveyed intentions are consistent with a study of actual evacuation behaviours in the subsequent 2016 Kaikōura earthquake and tsunami, providing validation for the survey indicators. This paper identifies the procedures least understood by the public and offers some solutions to improve tsunami preparedness.

Network Modeling of Hurricane Evacuation Using Data-Driven Demand and Incident-Induced Capacity Loss Models

The development of a hurricane evacuation simulation model is a crucial task in emergency management and planning. Two major issues affect the reliability of an evacuation model: one is estimations of evacuation traffic based on socioeconomic characteristics, and the other is capacity change and its influence on evacuation outcome due to traffic incidents in the context of hurricanes. Both issues can impact the effectiveness of emergency planning in terms of evacuation order issuance, and evacuation route planning. The proposed research aims to investigate the demand and supply modeling in the context of hurricane evacuations. This methodology created three scenarios for the New York City (NYC) metropolitan area, including one base and two evacuation scenarios with different levels of traffic demand and capacity uncertainty. Observed volume data prior to Hurricane Sandy is collected to model the response curve of the model, and the empirical incident data under actual evacuation conditions are analyzed and modeled. Then, the modeled incidents are incorporated into the planning model modified for evacuation. Simulation results are sampled and compared with observed sensor-based travel times as well as O-D-based trip times of NYC taxi data. The results show that the introduction of incident frequency and duration models can significantly improve the performance of the evacuation model. The results of this approach imply the importance of traffic incident consideration for hurricane evacuation simulation.