Evacuation Decision Research Articles

Modeling evacuation activities amid compound hazards: Insights from hurricane Irma in Southeast Florida

Given the destructive nature of hurricanes in tropical regions, pre-disaster evacuation has emerged as a critical approach for hurricane preparedness. Nevertheless, the compounding effects of natural hazards and the outbreak of infectious diseases, such as Covid-19, significantly challenge hurricane evacuation management. To investigate emergency responses under compound hazards, this study develops an activity-based model to measure the evacuation behaviors of individuals, using Hurricane Irma as a case study. Four scenarios are designed, including a single hurricane hazard, Hurricane Irma compounded with a pandemic like Covid-19, Hurricane Irma compounded with flood damage to the transportation network, and a combination of all these hazards. The metropolis-hasting algorithm is utilized to generate a population with socioeconomic attributes, which is then allocated to census block groups covering Palm Beach, Broward, Miami-Dade, and Monroe Counties in Florida. Datasets from multiple sources are used to measure evacuation decisions, which are subsequently simulated using MATSim. The results highlight the potential impacts of compound hazards on transportation systems, including increased congestions in scenarios involving compounded hurricanes and floods, especially between 10 a.m. and 7p.m. Moreover, a higher proportion of socially vulnerable populations is observed in scenarios involving compounded hurricanes and pandemics, particularly in the Key West area. The developed model could be further applied to measure the indirect impacts of natural hazards on transportation systems.

Design of an Emergency Medical Information System for Mass Gatherings

BackgroundThe frequency of mass gatherings is increasing. Such events often involve many people and carry the risk of mass casualty incidents, which require substantial medical resources from various healthcare institutions. The current medical system, while meeting daily needs, struggles to address the demand for a high volume of emergency resources and real-time data exchange. ObjectiveThe aim of this study was to develop an emergency medical information system for mass gatherings. MethodsWe developed an emergency medical information system for mass gatherings. Based on a unified prehospital and intrahospital emergency data exchange protocol, we can directly standardize medical information data and provide data support for the evacuation decision support algorithms of multiple institutions. Wearable devices, vehicle-mounted devices, video calling systems and surveillance systems are connected to capture real-time scenes. ResultsWe constructed the system via mobile applications and online platforms and deployed it in 3 hospitals, 5 ambulances and 17 on-site medical locations. We constructed a set of electronic medical records covering the whole first aid process according to the basic principles of first aid. The simulation results show that the proposed algorithm is suitable for mass gatherings. The overall survival rate of patients can be improved by 5%, and the average evacuation efficiency of patients can be improved by 50%. Furthermore, in a real-world environment, this method can ensure patient survival and achieve good convergence. ConclusionsOur system is capable of providing robust medical information support for emergency medical services during large-scale assembly events, ensuring a visualized full-process emergency response and decision-making for the diversion and subsequent transport of a large patient population.

Factors affecting household evacuation decision making in response to disaster: Case study from the 2021 South Kalimantan floods, Indonesia

Understanding the factors that influence evacuation decisions is crucial for optimizing resource allocation and formulating an effective emergency plan, particularly in the communication and implementation of warnings. To assist with this endeavour, this study investigates whether selected variables affected evacuation decision-making during the 2021 South Kalimantan, Indonesia, Floods. This is achieved through identifying significant factors in a binomial regression model constructed using a resident survey during the extreme floods of January 2021 and the more typical frequent floods of December 2021. The survey captures sociodemographic, capacity-related, hazard-related and warning-related factors from 375 residents including 205 in West Banjarmasin (urban area) and 170 in Tabuk River (rural area). It was found that families with partial ownership of their house (renting or living with another family) and those with a higher number of members are less likely to evacuate, as are those with a lack of knowledge about evacuation shelters and routes. External factors influencing people's evacuation decisions included the flood height and duration as well as whether there was an unofficial warning to evacuate. The findings suggest the following actions could assist with encouraging safety-seeking behaviour through evacuation: ensure evacuation instructions reach larger families and households and that they are relevant for them; include evacuation instructions with warnings; and conduct regular evacuation drills that include information about the flood risk map and the location and route to relevant evacuation shelters.

Adapting to Adversity: Unraveling the nexus between vulnerability and well-being in coastal Bangladesh

As climate change intensifies multi-dimensional vulnerabilities in coastal Bangladesh, understanding its impact on household well-being becomes crucial. This study empirically investigates how climatic extreme event-triggered (super-cyclone Amphan) vulnerabilities affect at-risk households' well-being in the Southwestern coastal zone. Deploying a systematic random sampling procedure, we collected data from 747 households using a structured questionnaire from seven disaster-affected Unions (i.e., sub-districts) from three districts, namely Khulna, Satkhira, and Bagerhat. The findings reveal that diverse demographic, socioeconomic, sociopolitical, disaster-preparedness, environmental, and locational factors influence multi-dimensional vulnerabilities in the study areas. Notably, these vulnerabilities significantly affected post-cyclone well-being, measured by consumption expenditure. Acquaintances with local political leaders (OR = 6.028, p < 0.05), participation in disaster-preparedness training (OR = 2.63, p < 0.01), and timely evacuation decisions (OR = 2.57, p < 0.01) emerged as key determinants of better household well-being following cyclone-disaster. Intriguingly, households with stronger political connections exhibited higher participation in disaster-preparedness training (z = 4.60, p < 0.000) and evacuation rates to safe havens (Chi2(1) = 5.14, p < 0.02). The study proposes three policy recommendations based on these empirical insights, paving the way for enhanced resilience and adaptive capacity in coastal Bangladesh communities facing the challenges of climate change.

What predicts hurricane evacuation decisions? The importance of efficacy beliefs, risk perceptions, and other factors

Risk theories and empirical research indicate that a variety of factors can influence people’s protective decisions for natural hazards. Using data from an online survey that presented coastal U.S. residents with a hypothetical hurricane scenario, this study investigates the relative importance of cognitive risk perceptions, negative affect, efficacy beliefs, and other factors in explaining people’s anticipated evacuation decisions. The analysis finds that multiple factors, including individual and household characteristics, previous experiences, cognitive and affective risk perceptions, and efficacy beliefs, can help predict hurricane evacuation intentions. However, the largest amount of variance in survey participants’ evacuation intentions is explained by their evacuation-related response efficacy (coping appraisals) and their perceived likelihood of getting hurt if they stay home during the storm. Additional analysis explores how risk perceptions and efficacy beliefs interact to influence people’s responses to risk information. Although further investigation in additional situations is needed, these results suggest that persuading people at high risk that evacuating is likely to reduce harm can serve as an important risk communication lever for motivating hurricane evacuation.

Influences of social networks, demographics, and evacuation duration on evacuation decisions: Insights from Ohio and Pennsylvania

Understanding post-hazard evacuation decisions poses challenges due to the complex interplay between individual behaviors and community influences. This study investigates the evacuation decisions made by residents from three counties of Ohio and Pennsylvania, correlating their choices with various sociodemographic and behavioral factors. By analyzing diverse evacuation durations—spanning a week, two months, and a permanent period—the research identifies patterns in distance preference, influenced by attributes such as gender, race, social networks, and travel habits. Evacuation duration emerged as a significant predictor of the distance traveled, with prolonged durations leading to farther evacuations. The research highlights the importance of leveraging local social networks for effective communication during hazards and informs policy recommendations for tailored evacuation advisories and inter-state evacuation collaborations.

Influencing Factors of Urban Public Flood Emergency Evacuation Decision Behavior Based on Protection Motivation Theory: An Example from Jiaozuo City, China

Public flood emergency evacuation is an important way to reduce casualties during a flood disaster. In this study, the influencing factors of urban public flood emergency evacuation decision behavior were studied using the protection motivation theory. The measurement index system of influencing factors of urban public flood emergency evacuation decision behavior was constructed from five dimensions (perceived susceptibility, perceived severity, self-efficacy, response efficiency, and response cost) based on the protection motivation theory. The impacts of key influencing factors on flood emergency evacuation decision behavior were analyzed using structural equation modeling. The results showed that (1) the susceptibility assessment and severity assessment of the public in threat appraisal had positive impacts on the process of flood emergency evacuation decision behavior. The direct effects of susceptibility assessment and severity assessment of the public on flood emergency evacuation decision behavior were 0.27 and 0.23, respectively. (2) The self-efficacy assessment and response efficiency assessment in coping appraisal also had positive impacts on the flood emergency evacuation decision behavior; the response efficiency had the greatest impact on the flood emergency evacuation decision behavior, and the standard path coefficients from response efficiency assessment and self-efficacy assessment to flood emergency evacuation decision behavior were 0.37 and 0.17, respectively. (3) The response cost assessment in coping appraisal had negative impacts on the flood emergency evacuation decision behavior, and its standard coefficient was −0.18. Finally, some strategies were proposed to improve the urban public flood emergency evacuation capacity based on this study.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

A stochastic optimization model for patient evacuation from health care facilities during hurricanes

We propose a rigorous modeling and methodological effort that integrates statistical implementation of hydrology models in predicting inland and coastal flood scenarios due to hurricanes and a scenario-based stochastic integer programming model which suggests resource and staging area decisions in the first stage and the evacuation decisions in the second stage. This novel study combines physics-based flood prediction models and stochastic optimization for large-scale multi-facility coordination of hospital and nursing home evacuations before impending hurricanes. The optimization model considers scenario-dependent evacuation demand, transport vehicles with varying capacities, and both critical and non-critical patients. Utilizing Hurricane Harvey of 2017 as a case study and actual healthcare facility locations in southeast Texas, we explore various evacuation policies, demonstrating the impact of routing strategies, staging area decisions, flood thresholds, and receiving facility capacities on evacuation outcomes. One of the findings is that choosing staging area(s) and deploying evacuation vehicles optimally considering the uncertainty of the hurricane’s path at the time of decision making could have significant effect on the total cost of the operation and evacuation time experienced by the evacuees. We also show the non-negligible value of the scenario-based staging and routing solution conservatively calculated in relation to a single scenario solution using the concept of value of stochastic solution.

Attachment and adjustment in expatriate reactions to the 2011 Tohoku disasters

PurposeThis study aims to develop an extended social attachment model for expatriates, integrating a multiple stakeholder perspective, to understand evacuation decisions during disasters.Design/methodology/approachThrough interviews with 12 Tokyo-based expatriates who experienced the 2011 Tohoku earthquake, tsunami and nuclear disasters, this study collects the lived experiences of a diverse set of expatriates. This data is analyzed abductively to map relevant evacuation factors and to propose a reaction typology.FindingsWhile the 2011 Tohoku disasters caused regional destruction and fears of nuclear fallout, Tokyo remained largely unscathed. Still, many expatriates based in Tokyo chose to leave the country. Evacuation decisions were shaped by an interplay of threat assessment, location of attachment figures and cross-cultural adjustment. The study also discusses the influence of expatriate types.Practical implicationsDisaster planning is often overlooked or designed primarily with host country nationals in mind. Expatriates often lack the disaster experience and readiness of host country nationals in disaster-prone regions in Asia and beyond, and thus might need special attention when disaster strikes. This study provides advice for how to do so.Originality/valueBy unpacking the under-researched and complex phenomenon of expatriate reactions to disasters, this study contributes to the fields of international human resource and disaster management. Specifically, seven proposition on casual links leading to expatriate evacuation are suggested, paving the way for future research.

Evacuation information methodology that combined a flooded environment and pedestrian behavioral model

Disaster information influences decision-making. Wireless internet and location-based services, aided by recent technological advances, have laid the foundation for providing personalized disaster information. Despite the provision of simple disaster forecasts and warnings, provision of specific evacuation actions (“where” and “how,” i.e., location and reachability of safe areas) and location-based personalized information that consider real-time disaster situations are a pressing priority. Thus, this study proposed and developed a three-step methodology for providing disaster information during floods that combined a flooded environment and pedestrian behavioral model, namely, 1) prediction of the flooded environment, 2) analysis of evacuation routes based on the movement time of evacuees according to the flood depth, and 3) provision of evacuation information by selecting the optimal evacuation route. This model was demonstrated in the Gangnam Station area in Seoul, Korea. Flooded environment prediction modeling of the target area was performed by applying the 100-year rainfall design to the Storm Water Management Model. The optimal evacuation route, according to the flood depth, was selected by applying the behavioral model (movement time calculation formula) for each evacuee derived through an evacuation behavior experiment. The pre- and post-flooding age- and gender-wise total evacuation distance and time were compared and analyzed. Post-flooding, the evacuation distance and time increased by 168.3% (381.34 m) and 172.3% (3.16 min) on average, respectively, indicating that evacuees must travel farther and longer. The analysis of post-flooding evacuation route change types revealed changes in evacuation distance and time in cases where shelters and evacuation routes and time were significantly different depending on evacuee characteristics (age and gender). This indicated that specific and visual disaster information can be provided according to evacuees’ age and gender using the proposed methodology. This study can serve as the basis for designing an advanced and personalized disaster information system that can provide specific evacuation actions to aid fast evacuation decision-making during floods.

Investigating factors underlying why householders remain in at-risk areas during bushfire disaster in Australia

Although most homes lack design and construction features to withstand bushfires, there is a growing trend of householders relocating to bushfire-prone areas. Notably, many bushfire-related fatalities have occurred within proximity, specifically within 100 m of bushland. Therefore, this paper explores the factors that drive householders to remain in at-risk areas, despite the imminent threat of bushfires. Thirty semi-structured interviews were conducted with participants residing in the southeastern region of New South Wales (NSW). The interviews were recorded, transcribed using Otter.AI, and subjected to thematic analysis using NVivo 12 Pro. The findings shed light on why certain householders decided to stay on their properties during the catastrophic 2019/2020 bushfires. Upon analysis, we uncovered thirty-six distinct factors that underlie householders’ choices to remain during bushfires. These factors were categorized into nine groups: protection-related, attitude-related, information-related, operation-related, road-related, shelter-related, finance-related, health-related, and rebuilding-related factors. The study underscores the importance of understanding gender-based differences and pet ownership in bushfire evacuation decisions, emphasizing the need for tailored communication strategies. These findings hold several important implications for research and practice regarding early self-evacuation from bushfires.

Short-term panel data analysis of the effect of flood risk communication on individual evacuation decisions

People-centered risk communication is important to mitigate the flood damage caused by the recent increase in heavy rainfall events in Japan. Longitudinal studies are particularly important for evaluating the effectiveness of risk communication methods; however, current research is insufficient. To address this gap, we conducted a longitudinal study, specifically through four panel surveys conducted over a short period, to investigate the effects of various risk communication methods such as running an evacuation simulation to learn about flood damage, providing information about the evacuation behavior of others, and distributing hazard maps. The results of a fixed effects analysis of the panel data suggest that the impact of risk communication depends on the initial evacuation attitude. In particular, we find that distributing hazard maps had a negative effect on the evacuation behavior of those who initially responded that they would evacuate. This suggests that residents in non-flood-prone areas may have acquired the correct hazard perception from these hazard maps. However, for those who initially chose not to evacuate, receiving the distributed content had a positive effect on their evacuation behavior 12 h before the typhoon hit. This suggests that those who initially chose not to evacuate may have reconsidered their decision. The findings of this study may help future risk communication by reducing congestion at evacuation sites due to excessive evacuation, while increasing the evacuation rate of those who should evacuate.

An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

Safety in nuclear energy utilization is crucial. In the event of a radioactive release incident, coupled with meteorological uncertainties, a radioactive plume can impact personnel evacuation. This paper presents an integrated solution for radionuclide release accident assessment and emergency evacuation decision making. The solution consists of three processes: atmospheric dispersion calculation, dose calculation, and path planning. The individual processes are connected through data exchange, thus allowing users to choose specific models based on experience. The proposed scheme combination is the Gaussian plume model, the dose conversion factor method, and an improved Dijkstra’s path planning algorithm. This algorithm, combined with dispersion and dose results, weighs nodes using the moving expected dose, facilitating the path with minimum dose risk. A program for Atmospheric Diffusion and Dose Calculation (ADDC) is developed based on the recommended scheme. Advantages include ease of use, minimal data requirements, data accessibility, and efficient evacuation. Dose estimates and optimal evacuation routes can be obtained quickly and at very low cost in response to rapidly changing environmental conditions. In a case study at a Chinese planned nuclear plant, we consider a spent fuel pool water loss scenario, assessing dose risks from 2020 to 2022 meteorological statistics. In dose calculation, results reveal that during an SFP drying accident, the radiation dose in the core area (100 m away) can reach 30–150 mSv within 2 h, and at 500 m away, it can reach 5–15 mSv. The dose in all downwind directions can drop below 250 mSv within 60 m. In path planning, results reveal the program is capable of accurately and efficiently calculating the minimum dose evacuation route. The program’s path reduces the effective dose by up to 67.3% compared to the shortest route, enhancino safety, and guiding post-accident decision making and planning.

Emergency evacuation based on long range communication technology

The complexity and uncertainty of a fire in a large public building with complex structure leads to risks during emergency evacuation. Therefore, it is crucial to conduct intelligent emergency evacuation according to the situational development of the accident environment, the crowding degree of evacuation routes, as well as the reliability and flexibility of data transmission in large complex building structures. In this study, we develop an Intelligent Emergency Evacuation System (IEES) based on the Long Range (LoRa) communication technology and the Evacuation Decision Optimization Model (EDOM). The IEES comprises three parts, i.e., multi-sensor Fire Monitoring System (FMS), Central Control System (CCS), and Wireless Evacuation Indication System (WEIS). The LoRa communication technology is used for data transmission among the three parts of the IEES to assist large scale evacuation. The EDOM is developed to support the CCS based on the improved adaptive ant colony algorithm. To facilitate the evacuation decision optimization, the initial node database in the evacuation spatial network is established intelligently on the Building Information Modeling (BIM) platform. An on-site application in a subway station with three underground floors is conducted. The results demonstrate that the IEES can guide people in evacuating from hazardous locations according to the state of the fire. Moreover, the phenomena of crowd retention and congestion at a few certain evacuation exits are considerably reduced to achieve global optimization of the evacuation path and accomplish the objective of intelligent emergency evacuation. Furthermore, WEIS can maintain normal operation and have good visibility in the fire environment of heat radiation and thick smoke. The IEES can realize dual wireless indication of vision and sound perception, and its response time is slightly affected by the building structure and transmission distance. The electromagnetic signal of electrical equipment and the operational state of vehicles in and out of the subway station causes the response time to a relatively larger lag. The response time of the IEES is delayed up to 14 ms. Nonetheless, it fulfills the requirements of response time of evacuation indication signs stated in the Technical Standard for Fire Emergency Lighting and Evacuate Indicating System.

Logistic regression vs machine learning to predict evacuation decisions in fire alarm situations

In this study we assessed logistic regression and machine learning models to explore their performance in predicting evacuation decisions and to provide readers with insights into the accuracy of these methods. We tested seven machine learning algorithms, including classification and regression tree, Naïve Bayes, K-nearest neighbours, support vector machine, random forest, extreme gradient boosting, and artificial neural network. We used data collected from 1,807 participants through web-based experiments to train and calibrate these models. The performance of each model was evaluated by area under the curve, accuracy, recall, specificity, precision, and F1-score. The results indicate that logistic regression had the highest area under the curve value (0.831), whereas extreme gradient boosting outperformed other machine learning models in terms of accuracy (0.780), specificity (0.810) and precision (0.820). K-nearest neighbours model had the greater recall (0.859) and artificial neural network the highest F1-score (0.785). The models identified that being with a close person was the most influential factor in the response to a fire alarm.

HEvOD: A database of hurricane evacuation orders in the United States

Assessing and improving the effectiveness of evacuation orders is critical to improving hurricane emergency response, particularly as the frequency of hurricanes increases in the United States. However, our understanding of causal relationships between evacuation orders and evacuation decision-making is still limited, in large part due to the lack of standardized, high-temporal-resolution data on historical evacuation orders. To overcome this gap, we developed the Hurricane Evacuation Order Database (HEvOD) – a comprehensive database of hurricane evacuation orders issued in the United States between 2014 and 2022. The database features evacuation orders that were systematically retrieved and compiled from a wide range of resources and includes information on order type, announcement time, effective time, and evacuation area. The rich collection of attributes and the resolution of the data in the database will allow researchers to systematically investigate the impact of evacuation orders, as a vital public policy instrument, and can serve as an important resource to identify gaps in current policies, leading to more effective policy design in response to hurricanes.

Uncovering non-emergency-related factors in threat responses: Logistic regression analysis of online experimental data for behaviour patterns

Delving into human behaviour during emergencies, this study investigates the evacuation decisions made by 1.807 individuals across fire alarm, explosion, and shooting scenarios. Through a series of 18 trials, encompassing diverse environmental and social influences, participants faced the decision to evacuate or stay. The impact of 20 influential factors on emergency decision-making was explored using logistic regression analyses. Across all threat scenarios, a significant majority of individuals opted to stay rather than evacuate. Our findings revealed a greater influence of factors in explosion trials compared to fire alarm and shooting scenarios. Age emerged as a consistent and significant determinant across all threats, while gender demonstrated significance exclusively within the explosion situations. These results illuminate the impact of non-emergency-related factors in shaping the evacuation decision process, providing with valuable insights for emergency management strategies.

Regression modeling of subjective probability of river inundation with epistemic uncertainty: a short-term panel data analysis

Lack of sufficient knowledge about flood risk can lead to “don’t know” responses or non-response in risk perception surveys, and incorrect treatment of these responses can lead to bias in the results. In this study, we focus on the possibility that the “50%” response actually means “fifty-fifty” and thus reflects epistemic uncertainty in the subjective probability of river flooding. We conduct an analysis that introduces a concomitant-variable latent class model as a method to adjust for this epistemic uncertainty. The results of the analysis accounting for epistemic uncertainty suggest that risk communication, such as simulated evacuation experiences and flood-related information distribution, increases subjective probability. Moreover, the proportion of latent classes with epistemic uncertainty decreased with each successive wave of the survey, thereby suggesting that knowledge acquisition and learning through the demonstration experiment led to a reduction in epistemic uncertainty. The analysis using the evacuation decision-making model also suggested that the introduction of subjective probability contributed to improving the likelihood of the model. These results suggest that knowledge acquisition through risk communication and short-term panel survey can lead to correct risk perception estimations and influence evacuation decisions.

Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods

Facing the escalating effects of climate change, it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management. Current studies in this area often have relied on psychology-driven linear models, which frequently exhibited limitations in practice. The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors, compared to existing models that mainly rely on psychological factors. An enhanced logistic regression model (that is, an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions (that is, univariate and bivariate threshold effects). Specifically, nonlinearity and interaction detection were enabled by low-depth decision trees, which offer transparent model structure and robustness. A survey dataset collected in the aftermath of Hurricanes Katrina and Rita, two of the most intense tropical storms of the last two decades, was employed to test the new methodology. The findings show that, when predicting the households’ evacuation decisions, the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability. This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.