Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation.

With evidence of increasing hurricane risks in Georgia Coastal Area (GCA) and Virginia in the U.S. Southeast and elsewhere, understanding intended evacuation behavior is becoming more and more important for community planners. My research investigates intended evacuation behavior due to hurricane risks, a behavioral survey of the six counties in GCA under the direction of two social scientists with extensive experience in survey research related to citizen and household response to emergencies and disasters. Respondents gave answers whether they would evacuate under both voluntary and mandatory evacuation orders. Bivariate probit models are used to investigate the subjective belief structure of whether or not the respondents are concerned about the hurricane, and the intended probability of evacuating as a function of risk perception, and a lot of demographic and socioeconomic variables (e.g., gender, military, age, length of residence, owning vehicles).

This dissertation investigates the evacuees' behavior under hurricane evacuation conditions and develops an agent-based travel demand model system for hurricane evacuation simulation using these behavioral findings. The dissertation econometrically models several important evacuation decisions including evacuate-stay, accommodation type choice, evacuation destination choice, evacuation mode choice, departure time choice, and vehicle usage choice. In addition, it explicitly considers the pre-evacuation preparation activities using activity-based approach. The models are then integrated into a two-module agent-based travel demand model system. The dissertation first develops the evacuate-stay choice model using the random-coefficient binary logit specification. It uses heterogeneous mean of the random parameter across households to capture shadow evacuation. It is found that the likelihood of evacuation for households that do not receive any evacuation notice decreases as their distance to coast increase on average. The distance sensitivity factor, or DSF, is introduced to construct the different scenarios of geographical extent of shadow evacuation. The dissertation then conducts statistical analysis of the vehicle usage choice. It identifies the contributing factors to households' choice of the number of vehicles used for evacuation and develop predictive models of this choice that explicitly consider the constraint imposed by the number of vehicles owned by the household. This constraint is not accommodated by ordered response models. Data comes from a post-storm survey for Hurricane Ivan. The two models developed are variants of the regular Poisson regression model: the Poisson model with exposure and right-censored Poisson regression. The right-censored Poisson model is preferred due to its inherent capabilities, better fit to the data, and superior predictive power. The multivariable model and individual variable analyses are used to investigate seven hypotheses. Households traveling longer distances or evacuating later are more likely to use fewer vehicles. Households with prior hurricane experience, greater numbers of household members between 18 and 80, and pet owners are more likely to use a greater number of vehicles. Income and distance from the coast are insignificant in the multivariable models, although their individual effects have statistically significant linear relationship. However, the Poisson based models are non-linear. The method for using the right-censored Poisson model for producing the desired share of vehicle usage is also provided for the purpose of generating individual predictions for simulation. The dissertation then presents a descriptive analysis of and econometric models for households' pre-evacuation activities based on behavioral intention data collected for Miami Beach, Florida. The descriptive analysis shows that shopping - particularly food, gasoline, medicine, and cash withdrawal - accounts for the majority of preparation activities, highlighting the importance of maintaining a supply of these items. More than 90% of the tours are conducted by driving, emphasizing the need to incorporate pre-evacuation activity travel into simulation studies. Households perform their preparation activities early in a temporally concentrated manner and generally make the tours during daylight. Households with college graduates, larger households, and households who drive their own vehicles are more likely to engage in activities that require travel. The number of household members older than 64 has a negative impact upon engaging in out-of-home activities. An action day choice model for the first tour suggests that households are more likely to buy medicine early but are more likely to pick up friends/relatives late. Households evacuating late are more likely to conduct their activities late. Households with multiple tours tend to make their first tour early. About 10% of households chain their single activity chains with their ultimate evacuation trips. The outcomes of this paper can be used in demand generation for traffic simulations. The dissertation finally uses the behavioral findings and develops an agent-based travel demand model system for hurricane evacuation simulation, which is capable of generating the comprehensive household activity-travel plans. The system implements econometric and statistical models that represent travel and decision-making behavior throughout the evacuation process. The system considers six typical evacuation decisions: evacuate-stay, accommodation type choice, evacuation destination choice, mode choice, vehicle usage choice and departure time choice. It explicitly captures the shadow evacuation population. In addition, the model system captures the pre-evacuation preparation activities using an activity-based approach. A demonstration study that predicts activity-travel patterns using model parameters estimated for the Miami-Dade area is discussed. The simulation results clearly indicate the model system produced the distribution of choice patterns that is consistent with sample observations and existing literature. The model system also identifies the proportion of the shadow evacuation population and their geographical extent. About 23% of the population outside the designated evacuation zone would evacuate. The shadow evacuation demand is mainly located within 3.1 miles (5 km) of the coastline. The output demand of the model system works with agent-based traffic simulation tools and conventional trip-based simulation tools. The agent-based travel demand model system is capable of generating activity plans that works with agent-based traffic simulation tools and conventional trip-based simulation tools. It will facilitate the hurricane evacuation management. / Ph. D.

Hurricanes have been considered one of the most costly disasters in United State, which lead to both economic loss and human fatalities. Therefore, understanding the characteristics of those who evacuated and of those who did not evacuate have been principal focus of some previous researches related to hurricane evacuation behavior. This research presents two sets of decision-making models for analyzing hurricane evacuation behavior, using two statistical methods: standard logistic model and mixed logistic model.The receipt of evacuation order, elevation, expenditure, the presence of children and elderly people, ownership of a house, and receipt of hurricane warning are found to be extremely important in evacuation decision making. When the mixed logistic model is applied, the rate of concern about hurricane threat is assumed to be random according to normal distribution. Mixed logistic models which account for the heterogeneity of household responses are found to perform better than standard logistic model.

abstract: Short-notice disasters such as hurricanes involve uncertainties in many facets, from the time of its occurrence to its impacts’ magnitude. Failure to incorporate these uncertainties can affect the effectiveness of the emergency responses. In the case of a hurricane event, uncertainties and corresponding impacts during a storm event can quickly cascade. Over the past decades, various storm forecast models have been developed to predict the storm uncertainties; however, access to the usage of these models is limited. Hence, as the first part of this research, a data-driven simulation model is developed with aim to generate spatial-temporal storm predicted hazards for each possible hurricane track modeled. The simulation model identifies a means to represent uncertainty in storm’s movement and its associated potential hazards in the form of probabilistic scenarios tree where each branch is associated with scenario-level storm track and weather profile. Storm hazards, such as strong winds, torrential rain, and storm surges, can inflict significant damage on the road network and affect the population’s ability to move during the storm event. A cascading network failure algorithm is introduced in the second part of the research. The algorithm takes the scenario-level storm hazards to predict uncertainties in mobility states over the storm event. In the third part of the research, a methodology is proposed to generate a sequence of actions that simultaneously solve the evacuation flow scheduling and suggested routes which minimize the total flow time, or the makespan, for the evacuation process from origins to destinations in the resulting stochastic time-dependent network. The methodology is implemented for the 2017 Hurricane Irma case study to recommend an evacuation policy for Manatee County, FL. The results are compared with evacuation plans for assumed scenarios; the research suggests that evacuation recommendations that are based on single scenarios reduce the effectiveness of the evacuation procedure. The overall contributions of the research presented here are new methodologies to: (1) predict and visualize the spatial-temporal impacts of an oncoming storm event, (2) predict uncertainties in the impacts to transportation infrastructure and mobility, and (3) determine the quickest evacuation schedule and routes under the uncertainties within the resulting stochastic transportation networks. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

Recent natural disasters have highlighted the need to evacuate people as quickly as possible. During hurricane Rita in 2005, people were stuck in queue buildups and large scale congestions, due to improper use of capacity, planning and inadequate response to vehicle breakdown, flooding and accidents. Every minute is precious in situation of such disaster scenarios. Understanding evacuation demand loading is an essential part of any evacuation planning. One of the factors often understood to effect evacuation, but not modeled has been the effect of a previous hurricane. This has also been termed as the 'Katrina Effect', where, due to the devastation caused by hurricane Katrina, large number of people decided to evacuate during Hurricane Rita, which hit Texas three weeks after Katrina hit Louisiana. An important aspect influencing the rate of evacuation loading is Evacuation Preparation Time also referred to as 'Mobilization time' in literature. A methodology to model the effect of a recent past hurricane on the mobilization times for evacuees in an evacuation has been presented utilizing simultaneous estimation techniques. The errors for the two simultaneously estimated models were significantly correlated, confirming the idea that a previous hurricane does significantly affect evacuation during a subsequent hurricane. The results show that the home ownership, number of individuals in the household, income levels, and level/risk of surge were significant in the model explaining the mobilization times for the households. Pet ownership and number of kids in the households, known to increase the mobilization times during isolated hurricanes, were not found to be significant in the model. Evacuation operations are marred by unexpected blockages, breakdown of vehicles and sudden flooding of transportation infrastructure. A fast and accurate simulation model to incorporate flexibility into the evacuation planning procedure is required to react to such situations. Presently evacuation guidelines are prepared by the local emergency management, by testing various scenarios utilizing micro-simulation, which is extremely time consuming and do not provide flexibility to evacuation plans. To gain computational speed there is a need to move away from the level of detail of a micro-simulation to more aggregated simulation models. The Cell Transmission Model which is a mesoscopic simulation model is considered, and compared with VISSIM a microscopic simulation model. It was observed that the Cell Transmission Model was significantly faster compared to VISSIM, and was found to be accurate. The Cell Transmission model has a nice linear structure, which is utilized to construct Linear Programming Problems to determine optimal strategies. Optimization models were developed to determine strategies for optimal scheduling of evacuation orders and optimal crossover locations for contraflow operations on freeways. A new strategy termed as 'Dynamic Crossovers Strategy' is proposed to alleviate congestion due to lane blockages (due to vehicle breakdowns, incidents etc.). This research finds that the strategy of implementing dynamic crossovers in the event of lane blockages does improve evacuation operations. The optimization model provides a framework within which optimal strategies are determined quickly, without the need to test multiple scenarios using simulation. Destination networks are the cause of the main bottlenecks for evacuation routes, such aspects of transportation networks are rarely studied as part of evacuation operations. This research studies destination networks from a macroscopic perspective. Various relationships between network level macroscopic variables (Average Flow, Average Density and Average speed) over the network were studied. Utilizing these relationships, a "Network Breathing Strategy" was proposed to improve dissipation of evacuating traffic into the destination networks. The network breathing strategy is a cyclic process of allowing vehicles to enter the network till the network reaches congestion, which is followed by closure of their entry into the network until the network reaches an acceptable state. After which entrance into the network is allowed again. The intuitive motivation behind this methodology is to ensure that the network does not remain in congested conditions. The term 'Network Breathing' was coined due to the analogy seen between this strategy to the process of breathing, where vehicles are inhaled by the network (vehicles allowed in) and dissipated by the network (vehicles are not allowed in). It is shown that the network breathing improves the dissipation of vehicle into the destination network. Evacuation operations can be divided into three main levels: at the origin (region at risk), routes and destination. This research encompasses all the three aspects and proposes a framework to assess the whole system in its entirety. At the Origin the demand dictates when to schedule evacuation orders, it also dictates the capacity required on different routes. These breakthroughs will provide a framework for a real time Decision Support System which will help emergency management official make decisions faster and on the fly.

Evacuation is oftentimes the best means to prevent the loss of lives when residents encounter certain hazards, such as hurricanes. Emergency managers and experts make great efforts to increase evacuation compliance but risk area residents may procrastinate even after making the decision to leave, thus complicating response activities. Purpose - This study explores the factors determining evacuees’ mobilization periods, defined here as, the delay time between the decision to evacuate and actual evacuation. The theoretical model that guides this research is built on the protective action decision model (PADM). It captures both the social and psychological factors in the process of transferring risk information to mobilization action. The psychological process of risk communication originates from personalized external information and ends with the formation of risk perception, ultimately influencing evacuees’ mobilizations. Design/methodology/approach – Using structural equation modeling (SEM), the model is tested using survey data collected from Hurricane Rita (2005) evacuees in 2006 (N = 897). The residents of three Texas coastal counties (Harris, Brazoria, and Galveston) are randomly selected and telephone-interviewed. Findings – The findings indicate that mobilizations are affected directly by respondents’ concerns of the threats to their personal lives and costs and dangers on their evacuation trips. The perceptions of evacuees can be related to their exposure, attention, and understanding of the risk information. Research limitations/implications – The results suggest that practitioners pay more attention on the residents’ understanding of different types of risks, their abilities to process the risk information, as well as the means information is delivered. Therefore the public authorities should be more active in protecting evacuees’ properties and assets, as well as encourage evacuees to take closer shelters to avoid potential costs on road. Also the community should be more involved in mobilizing evacuation, as long as social cues can assist evacuees to better comprehend the threat information. Originality/value – This study tests the PADM framework empirically and structurally. It also clarifies the definition of evacuation mobilization using a new operation based on the evacuation groups per household.

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