AbstractThe data on warning diffusion rates over time are generally sparse and are extremely limited for hurricanes. This is a potential problem for transportation analysts who are trying to comput...

Hurricane evacuation warnings from local officials are one of the most significant determinants of households’ evacuation departure times. Consequently, it is important to know how long after the National Hurricane Center (NHC) issues a hurricane watch or warning that local officials wait to issue evacuation warnings. The distribution of local evacuation warning issuance delays determined from poststorm assessment data shows a wide range of warning issuance delay times over an 85-h time span, although the vast majority of times fall within a 40-h window. Nearly 30% of the jurisdictions issued evacuation warnings before an NHC hurricane warning. Only 5% delayed the decision for more than 25 h after the NHC hurricane warning. The curves for warning issuance delays, using both the NHC watch and NHC warning issuance times as reference points, are very different from the warning issuance curves observed for the rapid-onset events. The hurricane data exhibit much more of an “S shape” than the exponential shape that is seen for rapid-onset data. Instead, curves for three different types of storm tracks, defined by a perpendicular/parallel dimension and a straight/meandering dimension, follow three noticeably different logistic distributions. The data also indicate that warnings were issued significantly earlier for coastal counties than for inland counties. These results have direct practical value to analysts that are calculating evacuation time estimates for coastal jurisdictions. Moreover, they suggest directions for future research on the reasons for the timing of local officials’ hurricane evacuation decisions.

Household evacuation preparation time distributions are essential when computing evacuation time estimates (ETEs) for hurricanes with late intensification or late changing tracks. Although evacuation preparation times have been assessed by expected task completion times, actual task completion times, and departure delays, it is unknown if these methods produce similar results. Consequently, this study compares data from one survey assessing expected task completion times, three surveys assessing actual task completion times, and three surveys assessing departure delays after receiving a warning. In addition, this study seeks to identify variables that predict household evacuation preparation times. These analyses show that the three methods of assessing evacuation preparation times produce results that are somewhat different, but the differences have plausible explanations. Household evacuation preparation times are poorly predicted by demographic variables, but are better predicted by variables that predict evacuation decisions-perceived storm characteristics, expected personal impacts, and evacuation facilitators.

Since Hurricane Katrina, transit evacuation service has been seen to serve critical needs in affected cities and an increasing number of hurricanes have struck the east coast where more people rely on public transportation to evacuate. Thus, it is important to model mode choice in evacuation for a better estimation of evacuation transit demand. In this study, a joint mode and destination type choice model was estimated based on multiple post-storm behavioral surveys from the northeastern seaboard to the Gulf coast. A Nested Logit model specification was used to estimate this joint choice model. The estimated model showed a significant linkage between mode and destination type choice, which validated the choice of a nested structure for the model. Selected variables include both household and zonal characteristics, reflecting the attributes of alternatives (e.g., hotel price and occupancy), the characteristics of households (e.g., residential stability and community density), and the interactions between them (e.g., average accessibility to a destination type). The use of multiple-storm data allowed the use of some variables that have not been considered in the past because of few variations in their values in a single dataset. Overall, the findings of this study provide insight into the factors affecting mode and destination type choice of residents during hurricane evacuation.

Large-Scale Evacuation introduces the reader to the steps involved in evacuation modelling for towns and cities, from understanding the hazards that can require large-scale evacuations, through understanding how local officials decide to issue evacuation advisories and households decide whether to comply, to transportation simulation and traffic management strategies. The author team has been recognized internationally for their research and consulting experience in the field of evacuations. Collectively, they have 125 years of experience in evacuation, including more than 140 projects for federal and state agencies. The text explains how to model evacuations that use the road transportation network by combining perspectives from social scientists and transportation engineers, fields that have commonly approached evacuation modelling from distinctly different perspectives. In doing so, it offers a step-by-step guide through the key questions needed to model an evacuation and its impacts to the evacuation route system as well as evacuation management strategies for influencing demand and expanding capacity. The authors also demonstrate how to simulate the resulting traffic and evacuation management strategies that can be used to facilitate evacuee movement and reduce unnecessary demand. Case studies, which identify key points to analyze in an evacuation plan, discuss evacuation termination and re-entry, and highlight challenges that someone developing an evacuation plan or model should expect, are also included. This textbook will be of interest to researchers, practitioners, and advanced students.

In this chapter, the authors describe aggregate changes in several measures of residential exposure to hurricane flooding for a sample of 89 Florida coastal communities in 15 counties and extrapolate their findings to the entire coastal area of the state. They also describe the impacts that these residential land use changes have had on hurricane evacuation clearance times and emergency shelter demand in a subset of our county sample. The authors begin by setting the context for this work with a discussion of Florida's growth management and comprehensive planning approach. Changes in land use within coastal communities alter the numbers of people living within hurricane hazard zones, as well as the types of people and their associated evacuation and sheltering behavior. Hurricane evacuation and shelter studies are conducted at the country and regional levels on a periodic basis under the auspices of eight of Florida's ten regional.

A time-dependent disaggregate destination choice model for evacuees from a hurricane is developed and tested in this paper. Using data from a survey conducted among South Carolina residents following Hurricane Floyd in 1999 as the basic information, dynamic information regarding the storm, the network, the destination zones, and decisions made by the emergency managers regarding the issuing of evacuation notices was added to the sample to provide a database that contained time-dependent data in 48 2-h time periods preceding landfall. Separate models were established for evacuees seeking shelter at the homes of friends and relatives and those who went to hotels and motels. The models describe destination choice in terms of time-dependent travel time between origin and destination, the amount of accommodation remaining in each zone in each time period, the estimated likelihood that the storm’s path takes it over a destination zone, the ethnic similarity between origin and destination zones, and the presence of a large metropolitan area and/or interstate highways in a destination zone. When estimating the performance of the models, their trip length frequency distributions are not significantly different to the observed trip length frequency distribution at the 95% level of significance. Applying the friend/relative model to the neighboring state of Georgia produced a trip length frequency diagram that had a similar pattern to the observed distribution but underestimated short-distance destinations and overestimated distant destinations.

The paper presents a dynamic gravity model to estimate time-dependent origin–destination (O-D) trip tables for hurricane evacuation with survey data from Hurricane Floyd in South Carolina in 1999. The objective is to test whether a dynamic gravity model with a revised impedance function can be used successfully to model hurricane evacuation destination choice. A static gravity model is used to estimate dynamic O-D matrices on the basis of which the dynamic gravity model is subsequently calibrated, because the dynamic O-D movement observed from the survey data is too sparse to allow estimation of the dynamic gravity model. The static gravity model was developed with a combined impedance function and describes evacuees' travel behavior. The model was estimated through a chi-square minimization process. The model was found to produce a trip length distribution that was statistically significantly similar to observed values. A time-dependent travel demand was estimated on the basis of a sequential logit model. With the trip distribution from the static gravity model and an expansion factor, time-dependent O-D trip tables were computed. Dynamic traffic assignment was then performed to provide time-dependent O-D travel costs. The static gravity model was extended into a time-dependent version in which the time-dependent travel cost, distance from the projected path of the hurricane to the destination, and remaining accommodation at the destination feature in the formulation. The model is solved by ordinary least-squares regression after transformation. The time-dependent gravity model was found to perform well by comparison of predicted and observed trip length distributions.

Hurricane Wilma in 2005 resulted in difficulties for Florida agencies in satisfying emergency relief demands by citizens. Because of this, a study was conducted to assess overall household preparedness for the aftermath of a disaster causing loss of electricity and other utilities for at least three days. Telephone interviews were conducted with 1200 Florida households to ask about current levels of preparedness in the spring of 2006 and about preparedness levels during the 2004 and 2005 hurricane seasons. Preparedness scores were computed based on a list of eight items for current preparedness and a list of ten items for preparedness for recent hurricanes. Results indicated that most households reported being well prepared to subsist on their own for at least three days following a disaster. Preparedness was strongly related to income, home ownership, race, age, and type of housing. Difficulties in meeting demand for emergency relief following Wilma appear to have resulted from needs of a relatively small percentage of households in an area having a very large population and from a number of households consuming relief supplies even though they reported being prepared.