Evacuation Choice Research Articles

Context matters when evacuating large cities: Shifting the focus from individual characteristics to location and social vulnerability

Climate change is driving extreme weather, heat and flooding, which increasingly require evacuations. Recent studies have found inconclusive results on the determinants of evacuation-related decisions and have reported widely varying evacuation rates, especially in high-density areas. We use a large dataset of geotagged evacuation choices in Paris, France (n = 2976) during a flood, to show that while they are rarely addressed, location, social vulnerability, length of residence, and hazard exposure are critical predictors. They can be used to infer the impact of previous experience on evacuation intentions, which is usually difficult to collect at scales relevant to decision-making. We address multiple evacuation choices over time that have previously been overlooked, including gradually self-fueling spontaneous evacuations after observations of peers evacuating or flooding proximity and post-impact evacuation due to infrastructure disruption. Our findings reveal that many people wait until their home is flooded to evacuate. The gap between the initial share of people leaving immediately and the minority refusing to leave in any case even after their housing is flooded gradually fills when considering different evacuation behaviors over time. Such chronology might explain the wide range of evacuation rates reported in the literature. However, people in vulnerable situations and with increased hazard exposure are more likely to declare that they would disregard evacuation instructions. One key implication is that policies aimed at reducing social vulnerability might be more effective than communication campaigns to increase preparedness and support evacuation.

Modeling competing guidance on evacuation choices under time pressure using virtual reality and machine learning

Efficient emergency guidance in buildings is essential for the safe evacuation of occupants. However, occupants may be exposed to contradictory information from signage and other sources of information. This study presents a set of forced-choice VR experiments and a machine learning approach to investigate the effect of competing or conflicting guidance on exit choice in simulated scenarios. In the VR study, participants chose between two potential exits under time pressure in each trial. Attracting cues (“EXIT” signs, audio instructions) and repelling cues (“DO NOT ENTER” signs, traffic cones) were placed in front of the two exits, either individually or in combination. In total, 2,125 datapoints were recorded from 20 participants. To model exit choice, machine learning (random forest, RF) models were applied to predict and interpret the guidance on evacuation choices. The tuned-hyperparameters RF model proposed in this study showed above 75% accuracy to predict evacuation choices facing conflict cues and was superior to default RF and logistic regression models. Interestingly, repelling cues such as “DO NOT ENTER” signs had a stronger impact on exit choice than attracting cues like “EXIT” signs when people have to make choices. Overall, the study offers valuable data and insights into exit choices, revealing that negative cues are more influential than positive ones in emergencies. These findings can significantly inform the design and optimization of egress guidance systems. This bias towards negative information under pressure suggests that evacuation systems should prioritize clear and prominent negative cues to guide occupants effectively.

Assessment of loss of life owing to dam-failure flooding considering population distribution and evacuation

Dam failures pose a serious threat to the safety of downstream populations. Scientifically assessing loss of life (LOL) from dam-failure flooding contributes to the emergency response. Population distribution varies spatially and temporally even within a day, and has different information dissemination and response speeds, which impacts the chances of safe evacuation. This study established an LOL assessment model that considers the initial population distribution and population evacuation process. Shared bike big data and other multi-source data were used to identify daily population activity. The evacuation process includes receiving warnings, responses, and horizontal or vertical evacuation choices, and the evacuation results were then evaluated. This model was applied to a dam in China. During nighttime, commuting, and working hours, the issuance of warnings 214 min, 137 min, and 0 min prior to dam failure, respectively, significantly mitigated LOL. Under the same communication and response speeds, the commuting period poses the highest risk of LOL. Assessments of LOL due to dam failure flooding need to consider the impact of the timing of dam failure and population distribution. The model used in this study proposes varying effective early warning times based on the time of dam failure occurrence, which can contribute to effective dam risk management, helping prevent personnel loss.

Estimating pre-impact and post-impact evacuation behaviors – An empirical study of hurricane Ida in coastal Louisiana and Mississippi

Evacuation after hurricane impacts appear (post-impact evacuation) has been underemphasized in empirical evacuation studies. This study uses well-examined factors for pre-impact evacuation and novel factors for post-impact evacuation in a sequential logit model for pre- and post-impact evacuation choices. Results show that the evacuation warning is the only factor that affected both pre-impact and post-impact evacuations. Demographics and housing characteristics are significant factors for pre-impact evacuation but not for post-impact evacuation, while residential damages and durations of utility outages are significant situational factors for post-impact evacuation. The durations of water and power outages had additive effects on the probability of evacuating after hurricane impact. Based on the results, we argue that the conventional assumption that sheltered-in-place residents will remain in the affected area, and the restoration planning and assistance generated with that premise will not be aligned with the demand of residents facing inhabitable living situations with damaged residences and prolonged utility outages. Agencies should consider extending the evacuation planning time horizon for storm events likely to induce severe damages and outages and prepare for evacuation during disrupted conditions.

Shortest or locally quickest? A prediction-based approach for evacuation choice simulation between multiple staircases

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice.

Identification of issues in disaster response to flooding, focusing on the time continuity between residents’ evacuation and rescue activities

Recently, there has been a growing concern regarding the severity of flood inundation, and the role of disaster response has become increasingly complex. As the number of problems to be addressed has been on the rise, the limits of individual disaster response capabilities have also been suggested. Therefore, disaster-response activities, whose contents change with time, should be regarded as a series of time-connected processes, and the issues of prolonged disaster responses should be reexamined. This study focused on the relationship between the evacuation of residents at the time of flooding and the subsequent rescue activities. Based on a flooding scenario in a real urban area, simulations were used to examine how different evacuation choices of residents may affect the subsequent rescue activities in the flooded area. The results show that the behavioral models desired in conventional studies of evacuation behavior do not necessarily lead to equally good outcomes throughout the entire disaster response. As a contribution of this study, a dilemma structure was found in which choosing risky evacuation behavior in the short term can ultimately improve the rescue situation. In addition, when residents themselves select their own evacuation behavior, it was found that the evacuation awareness of each resident affects the safety of other residents in the community and that the personal safety of a person is affected by the evacuation awareness of all community residents. It was suggested that not only the response of the government but also the attitude change of individual residents is important to solve this social dilemma.

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.

Risk perception of compound emergencies: A household survey on flood evacuation and sheltering behavior during the COVID-19 pandemic

Compound hazards are derived from independent disasters that occur simultaneously. Since the outbreak of COVID-19, the coupling of low-probability high-impact climate events has introduced a novel form of conflicting stressors that inhibits the operation of traditional logistics developed for single-hazard emergencies. The competing goals of hindering virus contagion and expediting massive evacuation have posed unique challenges for community safety. Yet, how a community perceives associated risks has been debated. This research utilized a web-based survey to explore the relationship between residents’ perceptions of conflicting risks and emergency choices made during a historic compound event, the flooding in 2020 in Michigan, US that coincided with the pandemic. After the event, postal mail was randomly sent to 5,000 households living in the flooded area, collecting 556 responses. We developed two choice models for predicting survivors’ evacuation options and sheltering length. The impact of sociodemographic factors on perceptions of COVID-19 risks was also examined. The results revealed greater levels of concern among females, democrats, and the economically inactive population. The relationship between evacuation choice and concern about virus exposure was dependent upon the number of seniors in the household. Concern about a lack of mask enforcement particularly discouraged evacuees from extended sheltering.

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.

Understanding California wildfire evacuee behavior and joint choice making

For evacuations, people must make the critical decision to evacuate or stay followed by a multi-dimensional choice composed of concurrent decisions of their departure time, transportation mode, route, destination, and shelter type. These choices have important impacts on transportation response and evacuation outcomes. While extensive research has been conducted on hurricane evacuation behavior, little is known about wildfire evacuation behavior. To address this critical research gap, particularly related to joint choice-making in wildfires, we surveyed individuals impacted by the 2017 December Southern California Wildfires (n = 226) and the 2018 Carr Wildfire (n = 284). Using these data, we contribute to the literature in two key ways. First, we develop two latent class choice models (LCCMs) to evaluate the factors that influence the decision to evacuate or stay/defend. We find an evacuation keen class and an evacuation reluctant class that are influenced differently by mandatory evacuation orders. This nuance is further supported by different membership of people to the classes based on demographics and risk perceptions. Second, we develop two portfolio choice models (PCMs), which jointly model choice dimensions to assess multi-dimensional evacuation choice. We find several similarities between wildfires including a joint preference for within-county and nighttime evacuations and a joint dislike for within-county and highway evacuations. Altogether, this paper provides evidence of heterogeneity in response to mandatory evacuation orders for wildfires, distinct membership of populations to different classes of people for evacuating or staying/defending, and clear correlation among key wildfire evacuation choices that necessitates joint modeling to holistically understanding wildfire evacuation behavior.

Hurricane Hazards, Evacuations, and Sheltering: Evacuation Decision-Making in the Prevaccine Era of the COVID-19 Pandemic in the PRVI Region

Abstract Although research relating to hurricane evacuation behavior and perceptions of risk has grown throughout the years, there is very little understanding of how these risks compound during a pandemic. Utilizing the U.S. territories of Puerto Rico and the U.S. Virgin Islands (PRVI) as a study region, this work examines risk perceptions and evacuation planning during the first hurricane season following the coronavirus disease 2019 (COVID-19) pandemic before vaccines were widely available. Analyses of how people view public shelters and whether evacuation choices will change in light of COVID-19 concerns were conducted, and results reflect major changes in anticipated evacuation behavior during the 2020 hurricane season. Key findings include that over one-half of the sample considered themselves vulnerable to COVID-19. When asked about their intended actions for the 2020 hurricane season, a significant number of individuals who would have previously evacuated to a shelter said that they would choose not to during the pandemic, reflecting that public shelter usage has the potential to decrease when the decision is coupled with COVID-19 threats. In addition, individuals were shown to have a negative perception of public shelter options. Approximately one-half of the respondents had little faith in shelters’ ability to protect them, and three-quarters of respondents found the risks of enduring a hurricane to be less than those posed by public shelters. These results will inform future hazard mitigation planning during a disease outbreak or pandemic.

Studying the Role of Personality Traits on the Evacuation Choice Behavior Pattern in Urban Road Network in Different Severity Scales of Natural Disaster

The study of evacuation behavior in response to disaster is necessary for emergency traffic management. As decision-making is not exclusively dependent on observable variables, in this research, it is attempted to study the evacuation choice behavior pattern in emergency response to earthquake disaster by considering both physical and behavioral factors. Personality traits are measured as behavioral latent factors by confirmatory factor analysis (CFA) of the short form of NEO-Five-Factor Inventory (NEO-FFI). A revealed preference survey with more than 700 samples was conducted in Qazvin city (Iran) which was based on real-life earthquake experience and the stated preference survey was conducted for six designated scenarios with different severities and times of earthquakes. Analysis of evacuation behavior is conducted by 3 types of discrete choice models (traditional binary logit model (TBLM), hybrid binary logit model (HBLM), and random parameters/mixed binary logit model (MBLM)). First, TBLM is estimated to study the effect of observable variables on response of people to earthquake disaster. Then, by adding the personality traits to modeling structure and constructing HBLM, the correct prediction percentage of the model increased. This study also considers heterogeneous mixtures of population in terms of income, family size, and five factors of personality traits by MBLM. The MBLM captures the heterogeneous responses of the respondent. By considering these variables as random parameters, the Log Likelihood function and pseudo square (ρ2) of the model increased.

Studying the Role of Behavioral Characteristics in Individuals Travel Choice Behavior in Response to Earthquake Disaster Using Discrete Choice Models

The study of decision-making in response to earthquake disaster including evacuation and destination choice behavior is necessary for emergency planners in order to provide emergency traffic management and solve evacuation problems in transportation network. In this research, it is attempted to study the pattern of choice behavior in Qazvin transport network by considering physical and behavioral factors simultaneously. The required data were collected through a questionnaire from 546 inhabitants of Qazvin based on the stated preferences (SP) survey for six designated earthquake scenarios at different severities and times. The effects of socioeconomic factors, trip daily characteristics, and behavioral factors on decisions to evacuate and choose destinations are studied in this research. The binary logit model is applied for analyzing the evacuation decision, and the multinomial logit model is applied for the destination choice in response to the earthquake disaster. The binary logit model analysis with 73% correct prediction indicates that socioeconomic characteristics including age, marital status, family size, and car ownership have positive effects on evacuation choice. Furthermore, the analysis of behavioral factors indicates that moderate religious belief decreases the likelihood of evacuation, while following previous experiences, trust in acquired trainings, and following decisions made by others moderately increase the likelihood of evacuation. Also, socioeconomic and behavioral factors are the determinants of the destination decision, and the results indicate that individuals following previous experiences highly are more likely to choose gas station and relative’s home destination versus going to urban open spaces.

Evacuation choice before and after major debris flows: The case of Montecito, CA

In 2018, a rare high magnitude debris-flows catastrophically impacted Montecito, resulting in human and material losses. Evacuations for rare natural hazards are complicated as residents have little or no experience with them. Therefore, understanding factors that influence evacuation choice for these rare debris-flows can help increase evacuation compliance and save lives. We employed mixed methods by using surveys and interviews. Data were collected after the disaster and addressed issues of evacuation behavior before and after the disaster. We employed Chi-squared, one-way Anova, and logistic regression to analyze the survey data, and we employed content analysis for the interviews. The evacuation choice did not significantly change before and after the disaster. Before the debris-flows, older respondents were more likely to evacuate. After, respondents who were worried about the debris-flows were more likely to evacuate. Residents who did not evacuate before and after the debris-flows based their decision on their home location and perceived risk. Before the debris-flows, impacted residents in the voluntary zone decided to remain due to the location of their residence (voluntary zone) and the influence of their social network. We recommend that municipalities carefully consider the type of natural hazard when defining evacuation zones and incorporate public awareness programs to educate and increase the risk perception of communities in hazardous areas, especially those threatened by rare natural hazards. In doing so, residents can rely on the knowledge learned from public awareness programs rather than their social network.

Earthquake Evacuation Choice and Management in a Developing Archipelagic Country—A Case Study of Surigao City, Philippines

The Philippines is a developing archipelagic country in Southeast Asia. The country is susceptible to multiple natural disasters, specifically earthquakes. This implies the significance of understanding earthquake evacuation choice in order to design effective planning and management of evacuation to minimize chaos, damage cost, and the loss of lives. This study investigated the determinants of earthquake evacuation and proposed earthquake evacuation planning and management in the Philippines, featuring the case study of Surigao City. The study used the primary dataset of 1055 observations gathered in 2019 and applied the nested logit model (NLM) to investigate the potential factors of earthquake evacuation decisions. We considered three output variables: evacuation choice, evacuation duration, and travel mode choice. We found that residents were more likely to evacuate their homes upon receiving an earthquake warning and move to a public shelter or open space. Additionally, respondents were more inclined to leave their homes when their houses suffered from moderate to severe/complete damage or when electricity and water supply were cut-off. Respondents were most likely to walk to evacuation centers as the majority of residents initially moved to the nearest open space immediately after an earthquake and stayed in an open space for less than 6 hours. No correlation was found between personal and household income factors with evacuation choice and travel mode choice. Furthermore, the study used the Analytical Hierarchy Process (AHP) to determine areas suitable for earthquake evacuation using insights from local government officials and planners. The areas identified for earthquake evacuation were developed to support evacuation planning and management.

A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study

Regret is often experienced for difficult, important, and accountable choices. Consequently, we hypothesize that random regret minimization (RRM) may better describe evacuation behavior than traditional random utility maximization (RUM). However, in many travel related contexts, such as evacuation departure timing, specifying choice sets can be challenging due to unknown attribute levels and near-endless alternatives, for example. This has implications especially for estimating RRM models, which calculates attribute-level regret via pairwise comparison of attributes across all alternatives in the set. While stated preference (SP) surveys solve such choice set problems, revealed preference (RP) surveys collect actual behavior and incorporate situational and personal constraints, which impact rare choice contexts (e.g., evacuations). Consequently, we designed an RP survey for RRM (and RUM) in an evacuation context, which we distributed from March to July 2018 to individuals impacted by the 2017 December Southern California Wildfires (n = 226). While we hypothesized that RRM would outperform RUM for evacuation choices, this hypothesis was not supported by our data. We explain how this is partly the result of insufficient attribute-level variation across alternatives, which leads to difficulties in distinguishing non-linear regret from linear utility. We found weak regret aversion for some attributes, and we identified weak class-specific regret for route and mode choice through a mixed-decision rule latent class choice model, suggesting that RRM for evacuations may yet prove fruitful. We derive methodological implications beyond the present context toward other RP studies involving challenging choice sets and/or limited attribute variability.

Influence of built environment and risk perception on seismic evacuation behavior: Evidence from rural areas affected by Wenchuan earthquake

During the decade after the 2008 Wenchuan earthquake, numerous earthquakes of different magnitudes have occurred in the affected areas. The need for immediate emergency evacuation following the occurrence of disaster was unanimously recognized by experts as the safest and most effective response of residents. Moreover, this behavior is directly influenced by residents' perceptions of disaster risk and built environment (BE). To explore the relationship between seismic evacuation behavior and perceptions of risk and BE, this study utilized a random survey in the form of a sample questionnaire in the areas most affected by the Wenchuan earthquake in 2008, combined with exploratory factor analysis and binary logistic regression analysis. Results show that residents' BE and risk perceptions positively affected their evacuation choice behavior. Specifically, when rural residents perceived a reasonable evacuation route and good quality of village roads, they would flee their homes as soon as an earthquake struck. With regard to seismic risk perception, if the residents perceived highly negative consequences from earthquakes, they would escape immediately as soon as an earthquake occurred. This conclusion highlights the importance of strengthening the construction of BE in disaster-prone areas, and raising residents’ awareness and risk perception of earthquake disasters. This study has practical significance in further construction of earthquake-stricken areas.

Earthquake Evacuation Choice Models Based on the Stated Preference Approach for Residents in Surigao City, Philippines

Earthquake Evacuation Choice Models Based on the Stated Preference Approach for Residents in Surigao City, Philippines

ANALISA PEMILIHAN MODA DAN WAKTU EVAKUASI BENCANA TSUNAMI DI KECAMATAN KUTA RAJA BANDA ACEH

Banda Aceh City, the capital of Aceh province, is particularly vulnerable to the tsunami. One of the most vulnerable sub-district in Banda Aceh is the Kuta Raja sub-district, which has a high tsunami threat level. Realizing the harmful effects that could be caused by the earthquake disaster. Therefore, disaster mitigation needs to be considered in order to reduce casualties and to minimize the risk of disaster that will occur in the future. One of the most important things in the evacuation planning is that mode and time of evacuation choices by the residents. Residents may be considered the safest mode and the correct time to evacuate. In this case, fastest mode and nearest escape building would be influenced factor for residents in selecting mode and time to evacuate. Therefore, in this study, we consider two mode choices, that is motorized and non-motorized transports. This study aims at analyzing the mode and time to evacuate choice made by the residents. The stated preference of questionnaire survey method is used to obtained necessary data set from the respondents. The discrete choice model namely binomial logit model is then governed to explore the behavioral response by respondents in selecting mode and time to evacuate. The result from the analysis reveals that the average time to evacuate less than 30 minutes was selected for the respondent who considers evacuating to the evacuation building within the radius of 15 km from their home. The probability to select a time to evacuate less than 30 minutes and evacuate distance within the radius of 15 km is about 71%. The remaining probability is around 29% to select a time to evacuate more than 30 minutes with evacuate distance more than the radius of 15 km. As for mode choice model depicts that the motorized transport is shown dominated up to 89% while for the non-motorized accounting up to 11%. The households characteristics that influencing the mode and time to evacuate selection are income, employment status, age and number of the child within a household.