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.

     

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.     

Hurricane evacuation demand models with a focus on use for prediction in future events

Although substantial literature exists on understanding hurricane evacuation behavior, few studies have developed models that can be used for predicting evacuation rates in future events. For this paper, we develop new ordered probit models for evacuation using survey data collected in the hurricane-prone state of North Carolina in 2011 and 2012. Since all covariates in the models are available from the census or based on location, the new models can be applied to predict evacuation rates for any future hurricane. The out-of-sample predictive power of the new models are evaluated at the individual household level using cross validation, and the aggregated level using available data from Hurricane Irene (2011), Hurricane Isabel (2003) and Hurricane Floyd (1999). Model results are also compared with an existing participation rate model, and a logistic regression model available from the literature. Results at the individual household level suggests approximately 70% of households’ evacuation behavior will be predicted correctly. Errors are evenly divided between false positives and false negatives, and with accuracy increasing to 100% as the percentage of people who actually evacuate goes to zero or all and decreasing to about 50% when the population is divided and about half of all households actually evacuate. Aggregate results suggest the new models compare favorably to the available ones, with average aggregate evacuation rate errors of five percentage points.     

Analysis of hurricane evacuee mode choice behavior

The purpose of this study is to explain the evacuee mode choice behavior of Miami Beach residents using survey data from a hypothetical category four hurricane to reveal different evacuees’ plans. Evacuation logistics should incorporate the needs of transit users and car-less populations with special attention and proper treatment. A nested logit model has been developed to explain the mode choice decisions for evacuees’ from Miami Beach who use non-household transportation modes, such as special evacuation bus, taxi, regular bus, riding with someone from another household and another type of mode denoted and aggregated as other. Specifically, the model explains that the mode choice decisions of evacuees’, who are likely to use different non-household transportation modes, are influenced by several determining factors related to evacuees’ socio-demographics, household characteristics, evacuation destination and previous experience. The findings of this study will help emergency planners and policy-makers to develop better evacuation plans and strategies for evacuees depending on others for their evacuation transportation.     

Leveraging social networks for efficient hurricane evacuation

One of the important factors affecting evacuation performance is the departure time choices made by evacuees. Simultaneous departures of evacuees can lead to overloading of road networks causing congestion. We are especially interested in cases when evacuees subject to little or no risk of exposure evacuate along with evacuees subject to higher risk of threat (also known as shadow evacuation). One of the reasons for correlated evacuee departures is higher perceived risk of threat spread through social contacts. In this work, we study an evacuation scenario consisting of a high risk region and a surrounding low risk area. We propose a probabilistic evacuee departure time model incorporating both evacuee individual characteristics and the underlying evacuee social network. We find that the performance of an evacuation process can be improved by forcing a small subset of evacuees (inhibitors) in the low risk area to delay their departure. The performance of an evacuation is measured by both average travel time of the population and total evacuation time of the high risk evacuees. We derive closed form expressions for average travel time for ER random network. A detailed experimental analysis of various inhibitor selection strategies and their effectiveness on different social network topologies and risk distribution is performed. Results indicate that significant improvement in evacuation performance can be achieved in scenarios where evacuee social networks have short average path lengths and topologically influential evacuees do not belong to the high risk regions. Additionally, communities with stronger ties improve evacuation performance.     

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.     

EMBLEM2: An empirically based large scale evacuation time estimate model

This article describes a simple, rapid method for calculating evacuation time estimates (ETEs) that is compatible with research findings about evacuees’ behavior in hurricanes. This revision of an earlier version of the empirically based large scale evacuation time estimate method (EMBLEM) uses empirical data derived from behavioral surveys and allows local emergency managers to calculate ETEs by specifying four evacuation route system parameters, 16 behavioral parameters, and five evacuation scope/timing parameters. EMBLEM2 is implemented within a menu-driven evacuation management decision support system (EMDSS) that local emergency managers can use to calculate ETEs and conduct sensitivity analyses to examine the effects of plausible variation in the parameters. In addition, they can run EMDSS in real time (less than 10 min of run time) to recalculate ETEs while monitoring an approaching hurricane. The article provides an example using EMDSS to calculate ETEs for San Patricio County Texas and discusses directions for further improvements of the model.     

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.     

National review of hurricane evacuation plans and policies: a comparison and contrast of state practices

The events of recent hurricane seasons have made evacuation a leading emergency management issue. In 1998 and 1999, Hurricanes Georges and Floyd precipitated the two largest evacuations in the history of the United States and perhaps, its two largest traffic jams. In response to the problems experienced during these events, many state departments’ of transportation have begun to take a more active role in the planning, management, and operation of hurricane evacuations. This is somewhat of a departure from prior practice when emergency management officials directed these tasks almost exclusively. Since the involvement of transportation professionals in the field of evacuation has been a fairly recent development, many of the newest practices and policies have only been used once, if ever. They also vary widely from state-to-state. To determine what the latest policies and strategies are and how they differed from one location to another, a national review of evacuation plans and practices was recently undertaken. The study was carried out from a transportation perspective and included both a review of the traditional transportation literature and a survey of department of transportation and emergency management officials in coastal states threatened by hurricanes. This paper highlights the findings of the survey portion of the study. It focuses mainly on current state practices, including the use of reverse flow operations and intelligent transportation systems. It also summarizes current evacuation management policies, methods of information exchange, and decision-making criteria. This paper presents the general similarities and differences in practices and gives particular attention to unique, innovative, and potentially useful practices used in individual states.     

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.     

A model of household time allocation taking into consideration of hiring domestic helpers

Household maintenance such as childcare not only induces activities and travel but also impose time constraints on individuals’ participation in other activities and travel. Instead of sharing household responsibilities, households may hire domestic helpers for household maintenance. Alternatively, they may get helps from members of the extended family such as parents of household heads. This paper develops a model to analyze households’ trade-offs between hiring domestic helpers for household maintenance and taking these responsibilities by household members. We will apply household economic theories to develop a time allocation model incorporating interactions among household members. We assume that households trade off the money they are willing to spend for hiring helpers with the time they may need to spend for household maintenance activities to maximize utilities, subject to time constraints. The model may be used to analyze the impacts of domestic helpers on household members’ time allocation to subsistence, maintenance and recreation activities. It may also be applied to analyze the impacts of government policies regarding the minimum salary of domestic helpers and the change of household members’ wage rates on households’ decision to hire helpers. The paper extends the current literature on intra-household activity–travel interactions by considering external helps from domestic helpers, which may contribute to the understanding of activity–travel patterns of household members.     

Evaluation of different contraflow strategies for hurricane evacuation in Charleston,South Carolina

Abstract

Limited specific evidence is available on the effectiveness of using contraflow as an evacuation traffic management tool. This study was conducted to determine the best combination of strategy options for evacuating Charleston, SC, along route I-26 during the event of a hurricane or other events. PARAMICS microscopic traffic simulator was used to evaluate the impact of each combination of evacuee response timing and traffic control strategy, such as contraflow, with respect to average vehicular travel time and evacuation duration. Analysis revealed the combination of management strategies that created the lowest evacuation durations and travel times for several types of anticipated evacuee responses. Furthermore, a proposed reconfiguration of the I-526/I-26 interchange for contraflow operations produced additional savings in travel times and evacuation durations. These findings support the use of all lanes for contraflow during all evacuations and provide justification to examine a possible reconfiguration of the I-526/I-26 interchange for use during evacuations.     

Modeling interdependencies between vehicle transaction,residential relocation and job change

This paper introduces a vehicle transaction timing model which is conditional on household residential and job relocation timings. Further, the household residential location and members’ job relocation timing decisions are jointly estimated. Some researchers have modeled the household vehicle ownership decision jointly with other household decisions like vehicle type choice or VMT; however, these models were basically static and changes in household taste over time has been ignored in nearly all of these models. The proposed model is a dynamic joint model in which the effects of land-use, economy and disaggregate travel activity attributes on the major household decisions; residential location and members’ job relocation timing decisions for wife and husband of the household, are estimated. Each of these models is estimated using both the Weibull and log-logistic baseline hazard functions to assess the usefulness of a non-monotonic rather than monotonic baseline hazard function. The last three waves of the Puget Sound Panel Survey data and land-use, transportation, and built environment variables from the Seattle Metropolitan Area are used in this study as these waves include useful explanatory variables like household tenure that were not included in the previous waves.     

Assessing traveler responsiveness to land and location based accessibility and mobility solutions

In this paper, a joint multinomial logit (MNL) model of residential location and vehicle availability choice is formulated and estimated using a sample of households from the San Francisco, CA area Metropolitan Transportation Commission's 1990 household travel survey. Subsequently, models of travel intensity (number of daily household trips and vehicle-miles traveled) are estimated as a function of household characteristics and of attributes derived from the joint residential location and auto availability choice model (number of vehicles, percent land developed). A policy test shows that reducing the cost of locating in the densest areas of the metropolitan area is likely to have only marginal impact on vehicle availability and household trip making.     

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.     

Unraveling the evacuation behavior of the medically fragile population: Findings from hurricane Irene

Despite the widely recognized importance of evacuation planning for residents with special needs – in this paper referred to as the medically fragile population – there is virtually no research available to guide such planning, as opposed to the numerous empirical research studies on the evacuation behavior of the general population. In this paper, we provide these long-overdue insights using data from a large-scale phone survey (over 7000 samples) conducted in the aftermath of hurricane Irene in the Hampton Roads region in Virginia. Via aggregate and disaggregate analyses, we start to unravel the behavior of this heavily understudied, and potentially vulnerable population group. Special emphasis will be placed on the differences between the medically fragile and non-medically fragile population. Two alternative definitions for what constitutes medically fragile are examined in this paper. Using the broader definition, it was found that a key difference between these two groups relates to the importance of having a strong network of family members in the area. When considering a more narrow definition, we found that being a single parent household, likelihood of neighborhood flooding and knowing most of the names of one’s neighbors have significantly different impacts on the two population groups.     

Analysis of child pick-up during daily routines and for daytime no-notice evacuations

In a no-notice disaster (e.g., nuclear explosion, terrorist attack, or hazardous materials release), an evacuation may start immediately after the disaster strikes. When a no-notice evacuation occurs during the daytime, household members are scattered throughout the regional network, and some family members (e.g., children) may need to be picked up. This household pick-up and gathering behavior was seldom investigated in previous work due to insufficient data; this gap in our understanding about who within families handles child-gathering is addressed here. Three hundred fifteen interviews were conducted in the Chicago metropolitan area to ascertain how respondents planned their response to hypothetical no-notice emergency evacuation orders. This paper presents the influencing factors that affect household pick-up and gathering behavior/expectations and the logistic regression models developed to predict the probability that parents pick up a child in three situations: a normal weekday and two hypothetical emergency scenarios. The results showed that both mothers and fathers were more likely to pick up a child under emergency conditions than they were on a normal weekday. For a normal weekday, increasing the distance between parents and children decreased the probability of parents picking up children; in other words, the farther parents are from their children, the less likely they will pick them up. In an emergency, effects of distance on pick-up behavior were significant for women, but not significant for men; that is, increasing the distance between parents and children decreased the probability that mothers pick up a child, but had a less significant effect on the fathers’ probability. Another significant factor affecting child pick-up behavior/expectations was household income when controlling for distance. The results of this study confirm that parents expect to gather children under emergency conditions, which needs to be accounted for in evacuation planning; failure to do so could cause difficulties in executing the pick-ups, lead to considerable queuing and rerouting, and extend the time citizens are exposed to high levels of risk.     

Risk-based spatial zone determination problem for stage-based evacuation operations

This study seeks to determine risk-based evacuation subzones for stage-based evacuation operations in a region threatened/affected by a disaster so that information-based evacuation strategies can be implemented in real-time for the subzone currently with highest evacuation risk to achieve some system-level performance objectives. Labeled the evacuation risk zone (ERZ), this subzone encompasses the spatial locations containing the population with highest evacuation risk which is a measure based on whether the population at a location can be safely evacuated before the disaster impacts it. The ERZ for a stage is calculated based on the evolving disaster characteristics, traffic demand pattern, and network supply conditions over the region in real-time subject to the resource limitations (personnel, equipment, etc.) of the disaster response operators related to implementing the evacuation strategies. Thereby, the estimated time-dependent lead time to disaster impact at a location and the estimated time-dependent clearance time based on evolving traffic conditions are used to compute evacuation risk. This time-unit measure of evacuation risk enables the ERZ concept to be seamlessly applied to different types of disasters, providing a generalized framework for mass evacuation operations in relation to disaster characteristics. Numerical experiments conducted to analyze the performance of the ERZ-based paradigm highlight its benefits in terms of better adapting to the dynamics of disaster impact and ensuring a certain level of operational performance effectiveness benchmarked against the idealized system optimal traffic pattern for the evacuation operation, while efficiently utilizing available disaster response resources.     

A dynamic model system of household car ownership,trip generation,and modal split: model development and simulation experiment

A dynamic model of household car ownership and mode use is developed and applied to demand forecasting. The model system consists of three interrelated components: car ownership, mechanized trip generation, and modal split. The level of household car ownership is represented as a function of household attributes and mobility measures from the preceding observation time point using an ordered-response probit model. The trip generation model predicts the weekly number of trips made by household members using car or public transit, and the modal split model predicts the fraction of trips that are made by public transit. Household car ownership is a major determinant in the latter two model components. A simulation experiment is conducted using sample households from the Dutch National Mobility Panel data set and applying the model system to predict household car ownership and mode use under different scenarios on future household income, employment, and drivers’ license holding. Policy implications of the simulation results are discussed.     

A comprehensive analysis of household transportation expenditures relative to other goods and services: an application to United States consumer expenditure data

This paper proposes a multiple discrete continuous nested extreme value (MDCNEV) model to analyze household expenditures for transportation-related items in relation to a host of other consumption categories. The model system presented in this paper is capable of providing a comprehensive assessment of how household consumption patterns (including savings) would be impacted by increases in fuel prices or any other household expense. The MDCNEV model presented in this paper is estimated on disaggregate consumption data from the 2002 Consumer Expenditure Survey data of the United States. Model estimation results show that a host of household and personal socio-economic, demographic, and location variables affect the proportion of monetary resources that households allocate to various consumption categories. Sensitivity analysis conducted using the model demonstrates the applicability of the model for quantifying consumption adjustment patterns in response to rising fuel prices. It is found that households adjust their food consumption, vehicular purchases, and savings rates in the short run. In the long term, adjustments are also made to housing choices (expenses), calling for the need to ensure that fuel price effects are adequately reflected in integrated microsimulation models of land use and travel.