Determinants of full and partial household evacuation decision making in hurricane matthew

This paper adds partial household evacuation to the traditional binary evacuate/stay decision. Based on data from a survey of Jacksonville, FL residents after Hurricane Matthew, multinomial (MNL) and random parameter MNL models were developed to determine the influential factors and whether some variables’ effects are more nuanced than prior literature suggests. The random parameter model was preferred to the fixed parameters model. Variables significant in this model included injury concern, certainty about hurricane impact location, age, marital status, family cohesion, and living in mobile or detached homes. Greater injury concern results in lower likelihood of none of the household evacuating and greater likelihood of partial evacuation, but lower likelihood of full household evacuation. Similarly, greater certainty about hurricane impact increased the probability of partial household evacuation but decreased the probability of full evacuation. Respondent age had heterogenous effects; for 85.54% of respondents, additional years of age increased the likelihood of the household staying. Married households had a higher likelihood of staying or evacuating together. Similarly, greater family cohesion was associated with the household remaining together. Living in mobile homes decreased the likelihood that all of the household stays or evacuates and increased the probability of partial household evacuation. Living in a single-family detached home was associated with lower likelihood of all of the household staying or evacuating and a greater likelihood of a partial household evacuation. These findings can inform strategies that influence full or partial household evacuations, material requirements based on these decisions, and ways to reduce family risk.     

A random-parameter hazard-based model to understand household evacuation timing behavior

The goal of this paper is to develop a random-parameter hazard-based model to understand hurricane evacuation timing by individual households. The choice of departure time during disasters is a complex dynamic process and depends on the risk that the hazard represents, the characteristics of the household and the built environment features. However, the risk responses are heterogeneous across the households; this unobserved heterogeneity is captured through random parameters in the model. The model is estimated with data from Hurricane Ivan including households from Alabama, Louisiana, Florida and Mississippi. It is found that the variables related to household location, destination characteristics, socio-economic characteristics, evacuation notice and household decision making are key determinants of the departure time. As such the developed model provides some fundamental inferences about hurricane evacuation timing behavior.     

Joint modeling of evacuation departure and travel times in hurricanes

Hurricanes are costly natural disasters periodically faced by households in coastal and to some extent, inland areas. A detailed understanding of evacuation behavior is fundamental to the development of efficient emergency plans. Once a household decides to evacuate, a key behavioral issue is the time at which individuals depart to reach their destination. An accurate estimation of evacuation departure time is useful to predict evacuation demand over time and develop effective evacuation strategies. In addition, the time it takes for evacuees to reach their preferred destinations is important. A holistic understanding of the factors that affect travel time is useful to emergency officials in controlling road traffic and helps in preventing adverse conditions like traffic jams. Past studies suggest that departure time and travel time can be related. Hence, an important question arises whether there is an interdependence between evacuation departure time and travel time? Does departing close to the landfall increases the possibility of traveling short distances? Are people more likely to depart early when destined to longer distances? In this study, we present a model to jointly estimate departure and travel times during hurricane evacuations. Empirical results underscore the importance of accommodating an inter-relationship among these dimensions of evacuation behavior. This paper also attempts to empirically investigate the influence of social ties of individuals on joint estimation of evacuation departure and travel times. Survey data from Hurricane Sandy is used for computing empirical results. Results indicate significant role of social networks in addition to other key factors on evacuation departure and travel times during hurricanes.

     

Managing evacuation networks

This paper proposes a non-anticipative, adaptive, decentralized strategy for managing evacuation networks. The strategy is non-anticipative because it does not rely on demand forecasts, adaptive because it uses real-time traffic information, and decentralized because all the information is available locally. It can be used with a failed communication network.The strategy pertains to networks in which no links backtrack in the direction of increased risk. For these types of networks, no other strategy exists that can evacuate more people in any given time, or finish the evacuation in less time. The strategy is also shown to be socially fair, in the sense that the time needed to evacuate all the people exceeding any risk level is, both, the least possible, and the same as if less-at-risk individuals did not participate in the evacuation. The strategy can be proven optimal even when backflows happen due to driver gaming.     

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.     

Do partners influence each other’s travel patterns? A new approach to study the role of social norms

To better understand the role of social norms in relation to people’s travel behavior this study addresses the question whether and to what extent partners in two-partner households influence each other’s travel patterns. For example, is the male household head more likely to start using the bicycle if the female household head also uses the bicycle (and vice versa)? While this is a straightforward question, it has, to the best of the author’s knowledge, not been explored in previous research. Using data from 958 couples from the German Mobility panel, the bidirectional effects between the travel patterns of male and female household heads are explored. To this end, the relatively new method of latent class transition analysis is used. The results show that, over time, travel pattern membership of the male household head influences travel pattern membership of the female household head and vice versa. Given that the effects are controlled for a range of individual and shared household characteristics, these results suggest that social norms at the household level play an important role. The paper concludes with an outlook on how the developed framework can be extended in the future.     

Household type and structure, time-use pattern, and trip-chaining behavior

In order to examine time allocation patterns within household-level trip-chaining, simultaneous doubly-censored Tobit models are applied to model time-use behavior within the context of household activity participation. Using the entire sample and a sub-sample of worker households from Tucson’s Household Travel Survey, two sets of models are developed to better understand the phenomena of trip-chaining behavior among five types of households: single non-worker households, single worker households, couple non-worker households, couple one-worker households, and couple two-worker households. Durations of out-of-home subsistence, maintenance, and discretionary activities within trip chains are examined. Factors found to be associated with trip-chaining behavior include intra-household interactions with the household types and their structure and household head attributes.     

Evacuee behaviors and factors affecting the tsunami trip generation model: A case study in phang‐nga,Thailand

A significant amount of research has focused on various types of evacuations, but little attention has been given to tsunami evacuation in the past. The purpose of this study was to investigate evacuee behaviors and factors affecting tsunami evacuation. The intention was also to analyze tsunami trip generation models. A data set of evacuation behavior was collected in an affected area, Baan Namkhem, Phang‐Nga Province, Thailand, following the Indian Ocean tsunami of December 26, 2004. The study was undertaken to determine evacuee response patterns in different conditions. Tsunami trip generation models were employed, using a binary logistic regression technique, to estimate the likelihood of evacuees being involved in each response pattern. It was found that the patterns of evacuee response to an emergency are different among the three conditions. Six factors (education level, ownership of the residence, distance to nearest seashore, disaster knowledge, number of household members, and status of respondent — permanent or transient) were found to be statistically significant. The results of this study can be used to improve the efficiency and effectiveness of future evacuation systems in Thailand.     

Multilevel analysis of daily time use and time allocation to activity types accounting for complex covariance structures using correlated random effects

In this paper multilevel analysis is used to study individual choices of time allocation to maintenance, subsistence, leisure, and travel time exploiting the nested data hierarchy of households, persons, and occasions of measurement. The multilevel models in this paper examine the joint and multivariate correlation structure of four dependent variables in a cross-sectional and longitudinal way. In this way, observed and unobserved heterogeneity are estimated using random effects at the household, person, and temporal levels. In addition, random coefficients associated with explanatory variables are also estimated and correlated with these random effects. Using the wide spectrum of options offered by multilevel models to account for individual and group heterogeneity, complex interdependencies among individuals within their households, within themselves over time, and within themselves but across different indicators of behavior, are analyzed. Findings in this analysis include large variance contribution by each level considered, clear evidence of non-linear dynamic behavior in time-allocation, different trajectories of change in time allocation for each of the four dependent variables used, and lack of symmetry in change over time characterized by different trajectories in the longitudinal evolution of each dependent variable. In addition, the multivariate correlation structure among the four dependent variables is different at each of the three levels of analysis.     

Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory

This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders.Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm and 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall.     

Household-level commuting mode choices,car allocation and car ownership level choices of two-worker households: the case of the city of Toronto

The paper presents a comprehensive investigation on household level commuting mode, car allocation and car ownership level choices of two-worker households in the City of Toronto. A joint econometric model and a household travel survey dataset are used for empirical investigations. Empirical models reveal that significant substitution patterns exist between auto driving and all other mode choices in two-worker households. It is revealed that, female commuters do not prefer auto driving, but in case of a one car (and two commuters with driving licenses) household, a female commuter gets more preference for auto driving option than the male commuter. Reverse commuting (commuting in opposite direction of home to central business district) plays a critical role on household level car allocation choices and in defining the stability of commuting behaviour of two-worker households. Two worker households in higher income zones and with longer commuting distances tend to have higher car ownership levels than others. However, higher transit accessibility to jobs reduces household car ownership levels. The study reveals that both increasing two worker households and reverse commuting would increase dependency on private car for commuting.     

A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection

Residential location search has become an important topic to both practitioners and researchers as more detailed and disaggregate land-use and transportation demand models are developed which require information on individual household location decisions. The housing search process starts with an alternative formation and screening stage. At this level households evaluate all potential alternatives based on their lifestyle, preferences, and utilities to form a manageable choice set with a limited number of plausible alternatives. Then the final residential location is selected among these alternatives. This two-stage decision making process can be used for both aggregate zone-level selection as well as searching disaggregate parcel or building-based housing markets for potential dwellings. In this paper a zonal level household housing search model is developed. Initially, a household specific choice set is drawn from the entire possible alternatives in the area based on the average household work distance to each alternative. Following the choice set formation step, a discrete choice model is utilized for modeling the final residential zone selection of the household. A hazard-based model is used for the choice set formation module while the final choice selection is modeled using a multinomial logit formulation with a deterministic sample correction factor. The approach presented in the paper provides a remedy for the large choice set problem typically faced in housing search models.     

On altruists and egoists in activity participation and travel: who are they and do they live together?

Formulation and specification of activity analysis models require better understanding of time allocation behavior that goes beyond the more recent within household analyses to understand selfish and altruistic behavior and how this relates to travel behavior. Using data from 1,471 persons in a recent 2-day time use/activity diary and latent class cluster analysis we identify 11 distinct daily behaviors that span from the intensely self-serving to intensely altruistic. Predicted cluster membership is then used to study within household interactions. The analysis shows strong correlation exists between social role and patterns of altruistic behavior. However, a substantial amount of heterogeneity is also found within social roles. In addition, travel behavior is also very different among altruistic and self-serving time allocation groups. At the household level, a substantial number of households contain persons with similar behavior. Another group of households contains a mix of self-serving and altruistic persons that follow specialized household roles within their households. The majority of households, however, are populated by altruistic persons. Single person households are more likely to be in the self-serving groups but not in their entirety. Altruism at home is directed most often toward the immediate family members. This is less pronounced when we examine altruistic acts outside the home. Konstadinos G. Goulias is a professor of Geography at the University of California Santa Barbara, has been a professor of Civil Engineering at the Pennsylvania State University from 1991 to 2004, and he is the founder and chair of the TRB task force on moving activity-based approaches to practice. Kriste M. Henson is a technical staff member at Los Alamos National Laboratory in the Decision Applications Division and is currently pursing a Ph.D. in Geography at the University of California—Santa Barbara.     

Effects of income inequality on evacuation,reentry and segregation after disasters

Large-scale disasters often trigger mass evacuation due to significant damages to urban systems. Understanding the evacuation and reentry (return) process of affected individuals is crucial for disaster management. Moreover, measuring the heterogeneity in the individuals' post-disaster behavior with respect to their socio-economic characteristics is essential for policy making. Recent studies have used large-scale location datasets collected from mobile devices to analyze post-disaster mobility patterns. Despite the availability of such data and the societal importance of the problem, no studies have focused on how income inequality affects the equity in post-disaster mobility. To overcome these research gaps, we overlay mobility data with income information from census to quantify the effects of income inequality on evacuation and reentry behavior after disasters, and the resulting spatial income segregation. Spatio-temporal analysis using location data of more than 1.7 million mobile phone users from Florida affected by Hurricane Irma reveal significant effects of income inequality on evacuation behavior. Evacuees with higher income were more likely to evacuate from affected areas and reach safer locations with less damage on housing and infrastructure. These differences were common among evacuees from both inside and outside mandatory evacuation zones. As a result of such effects of inequality, significant spatial income segregation was observed in the affected areas. Insights on the effects of income inequality on post-disaster mobility and spatial segregation could contribute to policies that better address social equity in pre-disaster preparation and post-disaster relief.     

Home-based trip end models — A comparison between category analysis and regression analysis procedures

The trip end models which have been used in past transportation studies are briefly summarised. Problems associated with the use of zone-based models are outlined and reasons are given to support the development of models at the household rather than zonal level.It is suggested that recent developments which have taken place in household-based models have not been entirely logical. In particular, arguments between regression models and category analysis models have been confused with the use of aggregate (zonal) as against disaggregate (household) data — regression models being associated with the use of zonal data and category analysis models with household data. Misunderstood arguments and false notions regarding sample sizes have directed attention from the regression analysis approach.A detailed comparison of the category analysis and regression analysis methods for developing household-based trip end models is given. Both methods have been applied using data from the Monmouthshire Land Use Transportation Study. The regression results reported are from a very preliminary analysis and contain a number of anomalies, although it is thought that sufficient work has been done to provide an objective evaluation of the two methods.It is recommended that the household regression approach should be further investigated since it has advantages as a modelbuilding procedure and makes better use of sample data. A certain amount of categorisation of household types is necessary and the investigations would attempt to determine the best balance between categorisation and regression fitting. Further development will be restricted if the trend towards minimum sample sizes of about 1000 households is continued. Larger samples should be taken in certain circumstances to pursue development work.     

Modeling household activity–travel interactions as parallel constrained choices

The daily activity-travel patterns of individuals often include interactions with other household members, which we observe in the form of joint activity participation and shared rides. Explicit representation of joint activity patterns is a widespread deficiency in extant travel forecasting models and remains a relatively under-developed area of travel behavior research. In this paper, we identify several spatially defined tour patterns found in weekday household survey data that describe this form of interpersonal decision-making. Using pairs of household decision makers as our subjects, we develop a structural discrete choice model that predicts the separate, parallel choices of full-day tour patterns by both persons, subject to the higher level constraint imposed by their joint selection of one of several spatial interaction patterns, one of which may be no interaction. We apply this model to the household survey data, drawing inferences from the household and person attributes that prove to be significant predictors of pattern choices, such as commitment to work schedules, auto availability, commuting distance and the presence of children in the household. Parameterization of an importance function in the models shows that in making joint activity-travel decisions significantly greater emphasis is placed on the individual utilities of workers relative to non-workers and on the utilities of women in households with very young children. The model and methods are prototypes for tour-based travel forecasting systems that seek to represent the complex interaction between household members in an integrated model structure.     

A threshold model of social contagion process for evacuation decision making

Individual evacuation decisions are often characterized by the influence of one’s social network. In this paper a threshold model of social contagion, originally proposed in the network science literature, is presented to characterize this social influence in the evacuation decision making process. Initiated by a single agent, the condition of a cascade when a portion of the population decides to evacuate has been derived from the model. Simulation models are also developed to investigate the effects of community mixing patterns and the initial seed on cascade propagation and the effect of previous time-steps considered by the agents and the strength of ties on average cascade size. Insights related to social influence include the significant role of mixing patterns among communities in the network and the role of the initial seed on cascade propagation. Specifically, faster propagation of warning is observed in community networks with greater inter-community connections.     

Residential mobility and location choice: a nested logit model with sampling of alternatives

Residential mobility and relocation choice are essential parts of integrated transportation and land use models. These decision processes have been examined and modeled individually to a great extent but there remain gaps in the literature on the underlying behaviors that connect them. Households may partly base their decision to move from or stay at a current location on the price and quality of the available alternatives. Conversely, households that are on the market for a new location may evaluate housing choices relative to their previous residence. How and the degree to which these decisions relate to each other are, however, not completely understood. This research is intended to contribute to the body of empirical evidence that will help answer these questions. It is hypothesized that residential mobility and location choice are related household decisions that can be modeled together using a two-tier hierarchical structure. This paper presents a novel nested logit (NL) model with sampling of alternatives and a proposed procedure for sampling bias correction. The model was estimated using full-information maximum likelihood estimation methods. The results confirm the applicability of this NL model and support similar findings from other empirical studies in the residential mobility and location choice literature.     

Estimating road network accessibility during a hurricane evacuation: A case study of hurricane Irma in Florida

Understanding the spatio-temporal road network accessibility during a hurricane evacuation—the level of ease of residents in an area in reaching evacuation destination sites through the road network—is a critical component of emergency management. While many studies have attempted to measure road accessibility (either in the scope of evacuation or beyond), few have considered both dynamic evacuation demand and characteristics of a hurricane. This study proposes a methodological framework to achieve this goal. In an interval of every six hours, the method first estimates the evacuation demand in terms of number of vehicles per household in each county subdivision (sub-county) by considering the hurricane’s wind radius and track. The closest facility analysis is then employed to model evacuees’ route choices towards the predefined evacuation destinations. The potential crowdedness index (PCI), a metric capturing the level of crowdedness of each road segment, is then computed by coupling the estimated evacuation demand and route choices. Finally, the road accessibility of each sub-county is measured by calculating the reciprocal of the sum of PCI values of corresponding roads connecting evacuees from the sub-county to the designated destinations. The method is applied to the entire state of Florida during Hurricane Irma in September 2017. Results show that I-75 and I-95 northbound have a high level of congestion, and sub-counties along the northbound I-95 suffer from the worst road accessibility. In addition, this research performs a sensitivity analysis for examining the impacts of different choices of behavioral response curves on accessibility results.