Collaborative Research: Agent-Based Simulation of Hurricane Evacuation
Decision-Making
I.
Objectives
This research will develop an agent-based model (ABM) of hurricane evacuation decisionmaking founded on the latest theoretical and empirical research into the socio-cognitive aspects
of evacuation behavior. In doing so, three major hypotheses will be examined: (i) that ABMbased evacuation simulations are transferable outside the regime where they are calibrated; (ii)
that these simulations can be used to corroborate existing qualitative models that may have
explanatory power; and (iii) agent-based models naturally generate clusters of individuals and
influential outliers that have clearly defined behavioral narratives.
Evacuation is a major component of hurricane response and is the most effective strategy in
mitigating human mortality related to hurricanes. Many of the policy options that can have a
significant effect on evacuation decisions and timing are influenced by traffic curvesfunctions
that describe the number of evacuees on the road at any given time). While sophisticated models
of evacuation traffic exist, the assumptions they make about the demand curves generating that
traffic are frequently far less sophisticated.
Those demand models that can accurately reproduce observed evacuation behavior are empirical
in nature and difficult to translate to other locales and events, while models of the individual
decision processes that generate traffic demand tend either to assume homogeneity of agency and
response across a region, or to be formulated in terms that are tied to the demographics of the
region. However, as was learned with Hurricanes Rita and Katrina, many groups that are similar
in terms of geographic vulnerability will not only respond differently to risk, but have different
ability to respond. Existing models do not capture the dependency of differential response on the
details of local demographics. Agent-based modeling provides a robust methodology for
addressing this problem of generalization.
The proposed model represents decision-makers, at the individual or household level, as
independent software entities. This approach scales well to different population sizes and
distributions of sub-population attributes; can capture emergent phenomena such as the
cumulative, collective influence of individual decisions within a social network; and is a natural
way of mapping the problem from the physical world to the realm of simulation, representing
correlations between demographics, geography, behavior, and decision-making in a natural and
realistic way. In addition, this approach supports simulation of alternative evacuation policies
such as public information strategies and organization of community-based evacuation networks.
Building on the results of Gladwin, Gladwin, and Peacock’s 2001 ethnographic model [1] of the
evacuation decision process, focus groups will be used to further elucidate the individual
decision-making algorithms used in evacuation. The agents will enact these decisions within a
temporal event structure compatible with the state-of-the-art empirical dynamic travel demand
model of Fu and Wilmot 2006 [2], which generates an evolving statistical description of
evacuation decisions. Using an agent-based framework to meld and generalize these two
approaches will result in a model that is consistent with the empirical models, but also has
enhanced explanatory power with regard to the factors and interactions of factors affecting
evacuation decisions. The ABM will also make explicit some of the implicit factors in each of
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these models, in particular the social networks that link individuals for communication and
provide them with access to opinion leaders. This opens the door for broader study and more
intuitive understanding of evacuation behavior, and may allow emergency planners to more
effectively anticipate and manage the demand-side aspects of hurricane evacuations and to more
appropriately target risk communication.
The proposed research will develop the computer simulation; conduct focus group studies on
evacuation decision-making to inform, refine, and validate the computer model; use modern
experimental designs to analyze the sensitivity of the model to different demographic and
circumstantial factors, developing understanding of their importance in policy-relevant ways;
apply the model to simulate the effects of alternative policies; and convene a cross-disciplinary
student seminar to communicate and explore the results from multiple perspectives.
II.
Background & Significance
Transportation planning/engineering models of evacuation, which are becoming increasingly
sophisticated, are hampered by relatively primitive demand models. This mismatch in the quality
and resolution of models limits opportunities for improving crisis management in evacuations.
Two key points of leverage are (i) understanding how to constructively influence demand, and
(ii) providing more efficient egress from impacted areas. Transportation planning/engineering
models address the latter point, and demand models of commensurate resolution and
responsiveness are needed to drive these transportation models.
Existing evacuation demand models (i) are not explanatory of the factors affecting evacuation
decisions, (ii) are not responsive to policy inputs, (iii) fail to explicitly account for crucial sociodemographic differences and social structures (e.g., social networks), (iv) ignore information
dissemination and flow, (v) cannot be transferred geographically or temporally to different
evacuations, and (vi) fail to quantify uncertainty. However, the best of them can successfully
reproduce individual evacuation decisions as elicited by post-storm interviews [1] and
approximate the collective time-dependent observed evacuation-departure times at a coarse level
[2].
Agent-based methods have proven useful for understanding the collective dynamics of large
groups of individuals engaged in similar behaviors [3]. They are especially useful for the study
of emergence, which is when a complex group behavior results from the interaction of simple
individual rules. Examples of emergent behavior include traffic jams [4], flocking in fish and
birds [5], and foraging in ants [6]. Agent-based modeling has also been successfully applied to
the study of human movement in building evacuations [7], yielding operational knowledge that
can be used to enhance survivability in an emergency.
Ideally, evacuation-demand models should be informed by the latest research in social and
cognitive theory. Moreover, with a sound research foundation of this kind, evacuation models
can more reliably predict responses to policy inputs. Responses to evacuation policies are formed
through social practices and cognitive processes. Demand-based models of evacuation policy
have not been sufficiently sensitive to social and behavioral variation, and in many cases have
failed to capture critical characteristics of population groups facing natural disasters. A unified
approach, which integrates social and behavioral research, agent-based demand models and
policy simulations, will help decision-makers in the public realm create more effective
evacuation policies.
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A.
Determinants of Evacuation Behavior
Central to appropriate transportation planning for evacuation is accurately gauging the demand
for roadway usage—that is, accurately accounting for the number of vehicles that will be in
transit on the roadways at any given time in the evacuation process. A number of evacuation
studies conducted under the aegis of the Army Corp of Engineers and Federal Emergency
Management Agency (FEMA) have consistently called for greater flexibility in modeling
differential behavior of sub-populations according to different contexts and storm scenarios [812]. This demand is based upon the decisions of individual households to evacuate. Research on
the determinants of hurricane evacuation has broadly fallen into three categories: research that
approaches evacuation decision making in terms of risk factors for evacuation failure, research
that explores the cognitive processes that are tied to specific action or to inaction (i.e. risk
perception and risk communication), and research on social vulnerability to disaster.
Differential responses to hurricane risk are well known. In his 1959 study on response to
Hurricane Audrey, Fogelman [13] found prior experience with storms, perception of risk,
communal and historic knowledge of hurricane response, social networks, and gender were
important factors in human behavior prior to and during the storm. However, despite increasing
understanding of the determinants of evacuation decision-making, models of hurricane
evacuation behavior tend to capture only a single dimension of this complex decision process,
often handling these variables linearly and in isolation.
Although explanatory frameworks and mechanisms differ across approaches, a number of factors
are consistently found to impact evacuation actions. These include socio-economic and
demographic features of the households, risk perception, and risk communication. While it is
understood that within a household, different individuals may evacuate separately or split
evacuation decisions (e.g. some members may chose to remain), research focused on those
factors which correlate to the behavior undertaken by the majority of household members shows
that evacuation decisions are influenced by size and type of household [14, 15], the presence of
children or elderly people [14, 16], pet ownership [15-17], gender of household head [15, 18],
ethnicity [14, 15, 19], and socio-economic status [14-16, 20-23]. These factors may serve as
proxy measures for access to transportation and other material resources that influence
evacuation behaviors [19, 24]. Additional fault lines for vulnerability include, age, poverty,
education, and other markers of socioeconomic status and reinforcement of warnings within the
familial or local social network. [13, 25-27].
Social factors that impact evacuation decisions include the importance of family networks,
sources of warning, and availability of non-governmental refuge [8, 28]. Other determinants
outside the household include risk perception [19, 29-32], event-related factors such as storm
severity and trajectory [17, 33, 34], and various aspects of risk communication [8-12, 15, 19, 29,
31].
No one factor appears preeminent in evacuation decision-making; in fact, research has revealed
myriad complexities. Geographic and storm aspects place households at risk. Material,
demographic, and experiential aspects of households influence both the desire and ability of the
household to evacuate. The decision-making process is influenced by fixed factors and by factors
that evolve along with the hurricane threat. These factors are further complicated in that they
speak to context- and event-specific characteristics of the household, the community, and the
hurricane. The factors that remain fixed over the course of a single evacuation event include
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socio-economic and demographic characteristics of the household, geographic situation, previous
storm and evacuation experience, and trust in hazard messengers. Those that change as a storm
approaches include risk communication, risk perception, hazard magnitude, community and
social network actions and resources, and response options. The confluence of the static and
dynamic features of household and community inform evacuation decisions and thereby impact
the evacuation demand curves.
B.
Critique of Evacuation Modeling
The landscape of hazard has repeatedly been shown to be structured by society and culture, but
there remains a disconnection between evacuation planning, risk assessment and social reality.
Despite our understanding of the complicated and dynamic processes involved in decisionmaking, hurricane-evacuation modeling has suffered from methodological inability to reproduce
interactions of heterogeneous systems and evolving system dynamics and feedbacks. Traditional
hurricane-evacuation models are based on biophysical properties of the environment (e.g.
whether or not an area is in a flood plain) and assume a homogeneous social character. The
reasons for this are two-fold.
First, evacuation models rely upon empirical studies of evacuation behavior. Many published
studies reflect publication biases that lean toward statistical models based on quantifiable data.
Qualitative work, by contrast, has shown that cultural context, social networks, social resources,
empowerment, and agency influence evacuation behavior through pathways that are not often
reflected in quantitative models. As a result, warnings, evacuation planning and timing, and other
policy measures for hurricane mitigation are targeted based on a false assumption of
homogeneity. The fallacy and costs of such assumptions were laid bare with the evacuation
response to Hurricane Katrina, in which sub-groups such as the elderly, disabled, and poor were
unable to get out of harm’s way.
A second factor contributing to the weak performance of evacuation modeling has been the
failure of evacuation planners and modelers to incorporate behavioral aspects of the human
population into their models. Sims and Baumann [35] denounced this practice twenty years ago,
faulting studies of evacuation for failing to understand factors that underlie the decision-making
process. It is striking that this disparity continues despite a much wider base of information and
more flexible methodologies to redress this shortcoming.
The differing analytic frameworks used to explore evacuation decision-making (i.e. risk factors
for failure, cognitive process, and vulnerability) yield varying interpretations of potential
causality and thereby suggest different mechanisms for changing evacuation outcomes. For
example, socioeconomic status or the material culture is considered a key determinant within the
context of vulnerability. Appropriate mechanisms for intervention in this dimension of
vulnerability therefore need to address a broad framework of social and economic development.
A different set of policy intervention points are suggested if the primary determinants of
evacuation are related to risk communication (i.e. timing of information, who delivers
information, and risk perception). The impact that social networks have on filtering
communication and moderating the influence of socio-demographic and material aspects
suggests still different intervention points. While social and cognitive theory can inform the
study of processes and intervention points, causality is suggested but not explicitly explored.
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C.
Strengths of Agent-Based Modeling
Agent-based modeling is an approach that allows for straightforward examination of causality
due to the very direct correspondence between real-world entities and elements of the model. An
ABM represents a real-world system as a collection of dynamically interacting rule-based
entities (the agents) [3, 6]. Agents are generally situated in space and time within an environment
that allows them to act. These kinds of models are a very natural way of representing social or
ecological systems where living things interact. An ABM is built by enumerating the
components of the system, creating “agents” whose attributes reflect those components, and
assigning each agent a set of behavioral rules that mimic the behavior of the corresponding
entity. To the degree that rules of behavior can be articulated, an ABM provides a very simple
way of exploring the interactions and implications of those rules.
ABMs are well suited for studying problems of emergence, where complicated collective
behaviors result from the interaction of many agents obeying simple rules [36]. The foraging
behavior of ants is one example of this kind of emergence, as are flocking behaviors in fish and
birds [37]. For example, realistic flocking can be produced by providing a group of moving
agents that follow three simple steering rules: (1) head towards the middle of the group, (2) head
in the same direction as your neighbors, and (3) avoid collisions. A collection of agents
following these rules will exhibit flocking as seen in the real world, without any need for
centralized control or high-level awareness or intentionality [5].
Agent-based modeling has been used to study the flow of foot traffic evacuating a building in an
emergency. This research demonstrated that a pillar placed a short distance off-center in front of
the main exit will significantly reduce jamming and increase the flow through secondary exits, a
result that can be used by architects to improve the fire safety of buildings [3, 7, 38]. Along with
biological systems like ant colonies [39], ABM techniques have also been used to study many
social systems and phenomena, such as vehicular traffic [40], civil violence [41], finance [42],
prehistoric settlement patterns [43], and transportation [44], to name only a few.
D.
Public Policy Requirements
Agent-based models have been used to test policy alternatives in a variety of planning
environments. For example, members of the research team have successfully applied an agentbased model of social characteristics of resort-community in-migrants to an evaluation of resort
housing policy [45], and an agent-based model of urban location decisions to an evaluation of
land use policies [46]. Agent-based policy models typically involve construction of scenarios as
alternative rule sets within the model. These scenarios are designed by distilling assessments of
current policy discussion and best practice alternatives into model elements. The outputs of these
scenarios are evaluated according to criteria defined through external research, such as
interviews with policymakers. This approach is a powerful method for exploring policy
alternatives because it directly maps diverse social characteristics to policy response [47] and
provides a framework for the simulation of policy constructs that illuminates key strategies and
dimensions of current debate [48-51].
III. Hypotheses
The chief advantage of agent-based models is their explicit treatment of causal pathways. The
hypotheses this research proposes to examine hinge on demonstrating the validity and efficacy of
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modeling causal processes. This project will test three hypotheses: (i) that ABM evacuation
simulations are transferable outside the regime where they are calibrated; (ii) that these
simulations can be used to corroborate existing qualitative models that may have explanatory
power; and (iii) that agent-based models naturally generate clusters of individuals and influential
outliers with clearly defined behavioral narratives.
A.
Transferability of Agent-based Evacuation Models
In general, a model or simulation may output a number of potentially observable variables.
Typically, statistical models such as sequential logit or survival analysis models produce output
for the small number of variables for which they were calibrated. Such models must be validated
against those same output variables, but with different input data sets. Having far more degrees
of freedom, agent-based models produce much more observable output: this opens the
opportunity for calibrating the model against one set of output variables, but validating it against
a broader set of variables. Additionally, ABMs supply predictions for output variables for which
real-world data collection would be impractical or prohibitively expensive. This happens because
ABMs generate state and time-series information for every characteristic of every individual in
the model: these data can be aggregated in arbitrarily complex ways. Two types of transferability
will be tested in the ABM evacuation simulation: (i) transferability of a model calibrated by the
historically observed evacuation demand curve for one hurricane to another hurricane in a
different geographic region; and (ii) validity of model output to historically observed variables
other than the evacuation demand curve for which the model was validated. Metrics developed to
compare historical observations to model output will enable this hypothesis testing. Essentially,
the test evaluates the generality of the model’s causal pathways along with the specifics of its
calibrated parameters.
B.
Corroboration of Explanatory Models
Existing literature on evacuation behavior offers several explanatory models that address risk
factors; cognitive processes associated with risk communication and actual evacuation (as
opposed to contemplated action); and social vulnerability. The feedbacks and causal pathways
between factors within these models remain theoretical. For example, social networks are
assumed to play a role in both mitigating lack of material resources for evacuation (i.e. social
vulnerability) and influencing behaviors through risk communication [19, 24, 32]. The proposed
ABM has the potential to corroborate or invalidate the behavioral assertions of the qualitative
models. To this end, comparing the output of structurally constrained versions of the ABM will
indicate the explanatory power of the qualitative models. For example, one can test the power of
the assertion that social networks play a key role in evacuation by comparing the predictive
strength of an ABM that allows social networks to influence behavior versus an ABM that
forbids social networks’ influence. In short, an ABM can provide a quantitative assessment of
the assertions various qualitative models make regarding the significance of various factors
affecting evacuation decision-making.
C.
Narratives for Clusters & Outliers
Since an ABM simulates the interactions of individual agents, it provides a great deal of
information regarding the history of simulated individual decision making. In fact, agent-based
models intrinsically produce narratives of individual behavior. Usually these individual decisions
and the outcomes of those decisions are aggregated into output variables that can be observed in
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surveys, traffic counts, censuses, etc. Because of the close integration of our focus groups with
the modeling effort, this project has the opportunity to qualitatively verify the ABM-generated
narratives with human “agents”. This research will test the hypothesis that the ABM-generated
narratives resonate with the narratives of potential evacuees in the field. In particular, clusters of
simulated individuals with similar decision histories will be identified; clearly defined narratives
describing those histories developed; and feedback solicited from focus groups regarding the
realism and completeness of these narratives. In addition to “closing the loop” between the
modeling effort and the agents being modeled, this will supply qualitative data on the validity of
the model, identify potential deficiencies, and suggest data collection strategies. Closely allied
with this effort will be the identification of influential “outliers”—for example, individuals (who
may represent opinion leaders or perhaps small groups of individuals) who exert disproportionate
influence on the simulated outcomes. Careful design of sensitivity studies will enable the
analysis of outliers, who are key targets for policy initiatives.
IV. Proposed Work
A.
Work in Progress and Model Development
Prior to this proposal, Dr. McGinnis and Dr. Bush developed a conceptual prototype ABM to
explore this idea in a preliminary way. The prototype contains statistically identical agents that
respond to generic time-dependent signals by increasing an evacuation readiness variable. When
this variable reaches a certain threshold, the agent decides to evacuate. Agents are connected in a
simple network that provides them with awareness of other agents’ evacuation decisions, which
is treated as another time-dependent evacuation signal. This model exhibits dynamics that are
sensible and realistic in a rough and qualitative fashion. This project will use the software
skeleton from this prototype as a starting point to speed the development of the model described
below. In this proposed effort, an agent-based model of a population’s collective hurricane
evacuation decisions will be developed as elaborated below.
Prototype ABM Behavior
10000
Agents
8000
Home
6000
Preparing
Traveling
4000
Safe
2000
0
20
15
10
5
0
Hours before Landfall
Figure 1. Agents in the simple prototype inhabiting different states over time.
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The household is the basic unit of decision-making and behavior in a hurricane evacuation;
therefore, in our model, each agent will represent a household. As stated in Section A, it is
acknowledged that individuals within a household may arrive at a split decision as to whether or
not to evacuate, but because evacuation research focuses on the decision that impacts the greater
part of the household members, it is reasonable to focus on households as representative of
household majority.
Each agent will have various socio-demographic and geographic attributes associated with it that
are relevant to its behavior, like income, pet ownership, elderly members of the household,
whether the house is in an evacuation zone, and so on. When the model is populated with agents,
randomly chosen (in a statistically precise manner) attributes that fit the demographic profile of
the modeled population will be assigned to the agents. For modeling large and heterogeneous
populations, this research will use multiple subpopulations, each of which will have its own
distinct demographic character.
These agents will use a decision-making algorithm that is similar to the ethnographic decision
tree model of Gladwin, Gladwin, & Peacock [1]. This algorithm is a tree of Boolean yes/no
decisions that are evaluated in sequence to determine what behavior the agent will exhibit:
whether it will evacuate or shelter in place, whether to leave now or prepare to leave later, and so
on. The decision steps depend on both the agent’s attributes and the current situation, so
demographic and circumstantial differences will affect the outcome. For example, each agent
considers, in turn, questions like: “Do I live in an evacuation zone? Has there been an evacuation
order? Do I have a place to go? Do I have money available for a hotel?” that lead up to a go/nogo decision; the answers to these questions will differ between agents and may change over time.
Historical
Preferences
Evacuee Focus
Groups
Social
Network
Peer
Communication
Ethnographic
Decision Tree
Models
Infrastructure
Sociocognitive
decisions
Decision
Tree
Demographic
Profiles
Hurricane
Public Information
Agents
Policy Analysis
Agent-Based Evacuation Model
Sequential
Decision
Models
External Data
Transferability of
Agent-based
Evacuation Models
Corroboration of
Explanatory Models
Narratives for
Clusters & Outliers
Hypotheses
Figure 2. Connections and influences in the proposed agent-based model of evacuation behavior.
The agents will make their decisions in a temporal framework similar to that of Fu & Wilmot’s
dynamic travel demand models [2, 52]. In these models, all the households that have not yet
evacuated are examined at each time step and assigned a probability of evacuating based on
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intrinsic and time-dependent variables such as time of day, hurricane speed, and whether the
residence is likely to flood. A fraction of the households are then evacuated according to this
probability. Our agent model will operate in a similar fashion, but instead of calculating a
probability of evacuation, each household’s decision to evacuate will be determined by
evaluation of the ethnographic decision tree.
In addition, this project will include the effects of interpersonal communication on the decisionmaking process by synthesizing a social network that allows the agents to communicate with one
another. Studies have shown that interpersonal communication, especially with regard to the
valued judgment of opinion leaders, is a significant factor in the decision to evacuate [53]. This
project will generate a carefully structured random network that is similar to observed networks
of social interaction, assign agents to it, and allow the agents to include the decisions of other
agents in their immediate network neighborhood in their decision-making algorithm [54-59].
Illustrative Agent-Based Evacuation Model
We use the index i  1,  , N to label each of the N agents in the simulation. Each agent
must be in one of five possible states:
si (t )  S  " HOME " , " PREPARING" , "TRAVELING" , " SAFE" , " DIVERTED",
where t indexes time steps in the simulation. All agents start the simulation at home:
i.e., si (0) " HOME " . The agents also have a quantitative measure of their “urgency” to
evacuation, ui (t ) , the threshold at which their urgency spurs them to action,  i , the
expected travel time to the safe zone,  i (t ) , and a temporal safety margin, mi ; for our
simple prototype model these are Gaussian IID random variables chosen at the start of the
simulation, t  0 . Additionally, each agent is embedded in a social network nij which we
represent in the following manner:
1 if agents i and j communicat e informatio n
.
nij  n ji  
0 if agents i and j do not communicat e informatio n
Finally, each agent evaluates or weighs information according to a function
f : I  
where
I  " landfall forecast" , " evacuation order" , " peer pressure" , etc.
are types of information an agent might receive.
The dynamics of agents is determined by the receipt of new information and by peer
pressure.
ui (t  1)  ui (t )   w  f ( g )  N (0,1)
( w, g )Gi ( t )
where Gi (t ) is the set of signals received by each agent at time t and N (0,1) is a
standard normal IID random variable; each signal ( w, g ) consists of a numerical strength,
w , and information type, g . The strength of most signals is given externally, but peer
pressure is computed from the average urgency of the agent’s peers in the social network:
g ipeer (t )   niju j (t )  nij .
j i
j i
The first time that ui (t ) exceeds i (t ) , the agent makes the transition from
si (t ) " HOME " to si (t  1) " PREPARING" ; additional rules handle other state
transitions and consider the expected time to landfall, the travel time to the safe zone, and
the temporal safety margin. This simple model also allows for the possibility of roadway
congestion forcing traveling evacuees to be diverted to unplanned shelter locations.
Figure 3. Simplified description of an evacuation-behavior ABM.
Melding different models in this
manner makes explicit a
number of factors that are
handled in an empirical,
implicit, or statistical way in
existing models. For example,
to evaluate perceived risk,
agents will aggregate
information about the
environment (e.g., type of
dwelling, whether it is in a
flood plain), the event (e.g.,
storm track, speed, and
intensity), and other relevant
signals (e.g., hurricane
warnings, traffic reports,
behavior of other agents) and
compare the result to an alarm
threshold to answer the
decision-tree question “do you
feel endangered by the storm.”
This approach makes it possible
to model response to
hypothetical future storms
based on presently available
data, rather than relying on
retrospective data like risk
perception that can only be
collected after a storm.
Model development will take place in parallel to the work with focus groups. It is expected that
this approach will create valuable synergy, with questions and insights arising in one arena
providing guidance in another. To this end, development will proceed iteratively, starting with a
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very simple, but functional, model and adding features one at a time, so that results from
intermediate modeling stages can inform research with focus groups and vice versa.
B.
Calibration & Validation
The agent-based evacuation-demand model will have only a few “free” tunable parameters—
most of its input data will be based on the characteristics of the synthetic population, resource
constraints (i.e. vehicle availability, road network status, etc.), and exogenous variables such as
the hurricane track and intensity. Because sufficiently detailed evacuation time-series data for
hurricanes is scarce, this model will use standard cross-validation techniques [60] to calibrate the
ABM’s free parameters and quantify its prediction errors: the set of available
calibration/validation data will be separated into “training” and “testing” partitions, and the
“training” sets will be used for calibration while the “testing” sets are used for validation. As the
training-testing partition is varied, the accumulated statistics will indicate the prediction error.
The actual calibration will use the “downhill simplex” method [61] or possibly a genetic
algorithm [62] to minimize the difference between the predicted and observed evacuationdemand curves for multiple demographic groups and geographic zones as the ABM’s tunable
parameters are varied. Stratifying the comparison ensures that the calibrated ABM parameters
are applicable to the full spectrum of households in the simulation. The calibrated ABM will be
compared with published calibrations of sequential logit and survival analysis models [2, 52].
Additional metrics indicating the quality of the model will be developed to the extent that
empirical data supports their computation.
C.
Qualitative Behavior & Sensitivity Analysis
The first step in analyzing the ABM is to thoroughly map out the various behavioral regimes
exhibited by the simulation, and examine their qualitative plausibility in terms of the feedback
loops present in the system. A significant product of the analysis will be a phase diagram
delimiting various possible “decision regimes” and identifying the possible phase transitions
between them. Statistical methods for experimental design provide a structured methodology for
performing this exploration. This research will use Latin hypercube sampling (LHS) and
orthogonal arrays (OA) as the basis of these designs [63, 64]. Because of the speed of the ABM
simulations, it will be possible to run large designs with numerous replications (necessary
because of the stochastic nature of the ABM). The research team believes it will be practical to
implement strength three (and higher) orthogonal array designs in this study—this enables us to
study three-way correlations in input parameters.
Initially, the researchers will example the sensitivities of parameters and processes internal to the
ABM model. Key candidate variables for sensitivity analysis are those related to awareness of
risk, expected travel times, destination constraints, and evacuation preparation factors. Variance
decomposition methods [65] will be used to identify the dependences of the model output on
individual factors. As part of this analysis, input factors that are highly correlated (e.g., not fully
independent) will be identified, as will factors to which the output is insensitive. A thorough
understanding of sensitivities will have benefits for model refinement, data collection & survey
design, and developing mitigation strategies.
Model sensitivity to factors left outside the models and to random events will be evaluated. It can
be argued that a particular crisis scenario can have multiple materially different outcomes due to
factors outside anyone’s control (such as an accident clogging a major evacuation route, wrong
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information about the feasibility of a particular evacuation path, or the specific structure of social
interactions in the affected area, all of which will alter how people heed evacuation orders)—a
social systems “butterfly effect”. In such situations, prediction may not be feasible, but a
simulation may allow investigation of the set of possible outcomes. Given constraints like those
imposed by the transportation system, the number of qualitatively different outcomes may not be
large. Furthermore, those outcomes may serve as guidance for developing flexible emergency
response plans usable in a variety of circumstances, rather than attempting to design a single
“optimal plan”.
The research will move beyond sensitivity analysis to quantify uncertainties present in the model
to the extent prior probabilities can be assigned to key model parameters. Uncertainties are
conditioned on external factors such as hurricane track and intensity. Latin hypercube sampling
on these prior probability distributions is the most appropriate method for quantifying ABM
output uncertainty. Because the model is stochastic, several replications may be needed in the
experimental design. The impact of varying the model structure will be examined in order to
elicit information about structural and systemic uncertainties in the ABM. The value of such
rigorous quantification of uncertainty lies in its providing guidance about the plausible range of
outcomes, rather than just a single estimate (or unquantified group of estimates) for outcomes
simulated by the agent-based model.
D.
Focus Groups
During the early stages of the project, focus groups will be formed to elaborate the factors,
interactions, and cognitive pathways that influence evacuation decision-making. The results from
these studies will feed into the model development process. As the ABM is refined and
elaborated, a second phase of focus groups will be held to validate the model. This critical
second phase reflects a small degree of community-based participatory research. The
communities that inform the model development will be given the opportunity to vet it and
discuss its possible utility. Through this, researchers hope to build a synergistic interaction
between the human narrative, the knowledge gained through qualitative exploration of the lived
experience, and analytic power of mathematical modeling.
The focus groups will draw from individuals currently residing in the greater New Orleans and
greater Galveston areas. Ideally the same cohort will be used for both types of focus groups, but
this may prove infeasible, especially in the rapidly changing social landscape of New Orleans. A
total of 10 focus groups, five in each of the study sites, will be realized for each phase for a total
of 20 groups. Each focus group will consist of 8-12 individuals.
Local coordinators from both New Orleans and Galveston will be employed to assemble focus
groups that feature known modifiers of evacuation behavior, reflecting different genders,
ethnicities, social classes, and household sizes and compositions. (See Letters of Support.) While
the financial scope of this proposal does not allow for creating ethnographic records of the focus
group participants, we hope to obtain supplemental funding to conduct semi-structured
interviews with each participant so that we may obtain a more robust ethnographic record of
evacuation timing and decision-making.
New Orleans and Galveston share a risk profile in that they lie within an active hurricane zone
and have experienced destruction from storms of historic proportion. The population of both
areas has recent experience with major evacuation efforts related to hurricanes Katrina and Rita.
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In addition, the research team has several years of combined experience working with local
communities in these two regions. In addition to receiving her Ph.D. from Tulane University in
New Orleans, Dr. Johnson worked on several projects within low-income, minority communities
in New Orleans prior to Hurricane Katrina and is part of an ongoing research project on resource
loss and coping behaviors. In 2006, Brian Muller and Seth McGinnis worked with Robert
Harriss, Director of the Houston Advanced Research Center, on the Galveston Futures project.
This project was a community design charette that brought together representatives from many
sectors of the Galveston community, public and private, to develop plans for the redevelopment
of public lands focusing on issues of sustainability and hazard mitigation.
E.
Synergies between Agent-Based Modeling and the Use of Focus Groups
The ABM will create pathways through which different agents navigate the evacuation event. In
essence, each agent represents a household and will generate a narrative of its journey and
interactions through the system. Researchers will extrapolate from the agents’ movements to
create a human narrative of information and interaction in the changing landscape of the
community evacuation. Narratives of typical or highly influential agents will be presented to
community members for them to assess how realistically the interactions, imputed judgments,
and ensuing behavior mirror their lived experiences. In turn, the narratives generated by
community members in the focus groups will be compared to agent behavior within the model to
verify that the decision-making algorithms accurately reflect the human decision process. The
degree of correspondence between the two types of narrative and ease with which the decision
processes can be translated from the domain of explanation to the domain of simulation will give
insight into the validity of the hypothesis that the agent-based model generates narratives that
resonate with potential evacuees.
F.
Education and Dissemination
The major outcomes of the modeling will be disseminated on three tiers: local community,
(inter)national research community, and academic institutions. The focus group mechanism
provides a valuable tool for researchers to share and validate the results of the modeling. The
results of this study will further be submitted to peer-review publications.
One of the more valuable educational components of this research is the involvement of students
in graduate level programs at the University of Denver (DU) and the University of Colorado,
Denver (CU). As the project nears completion, a cross-institutional colloquium targeting
graduate and post-graduate students will be held. The intent is to bring together students from the
DU Graduate School of International Studies who are focusing on issues of disaster preparedness
and homeland security (mostly master’s degree students, some Ph.D. candidates), and urban
planning students from CU (mostly master’s level students), with evacuation modelers and
transportation planners to discuss the utility of the ABM and issues surrounding evacuation
planning.
V.
Broader Impact & Significance
This project may have significant impact on the way in which evacuation planning is undertaken,
as it will account for heterogeneity in evacuation decision responses and context-specific
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variables, both of which have not been elaborated upon in prior evacuation planning models. By
providing a model of evacuation that can explain the effectiveness of various policies and
contextual factors, this research will enable evacuation planners to make more informed and
effective decisions. This project speaks to the NSF concern for the integration of science
research and education through: (1) a collaborative design which integrates feedback
mechanisms between researchers and lay community members as noted in Section E above, and
(2) a day-long, multidisciplinary seminar on evacuation decision-making which will unite
graduate students from urban planning, public policy, and disaster planning. Working with the
lay public to validate the cognitive choices which occur in the evacuation decision-making
process is the first step in what is believed to be an on-going dialogue. Once the model is
complete, the researchers will seek additional funding to begin working with urban planners to
further refine the tool and introduce it into practice.
The proposed work nicely complements several ongoing initiatives and addresses
recommendations of national and international organizations: The National Science Board’s
Task Force on Hurricane Science and Engineering has identified “human behavior and risk
planning” and “evacuation planning” [66] as high priority research investments. Furthermore, the
Transportation Research Board’s Emergency Evacuation Subcommittee identified, in its January
2006 meeting, [67] the incorporation of human behavior into evacuation modeling as critical to
effective evacuation prior to events like hurricanes. Finally, the World Meteorological
Organization’s THORPEX program includes a Societal and Economic Applications subprogram
[68] that has identified the societal impacts of high impact weather as a key research topic.
VI. Management Plan
All senior members of the project team are based in the greater Denver area. This geographic
proximity allows for ease in meeting on a monthly basis or more frequently, as necessary.
Further, the team will keep in contact through phone and e-mail.
Each institution will be responsible for managing its portion of the grant. Dr. McGinnis, the P.I.,
located at the National Center for Atmospheric Research (NCAR), will oversee proposal
performance under the guidance of Dr. Bush.
A.
Investigator Roles and Experience
Seth McGinnis, principal investigator, will develop the agent-based model, perform the model
runs, and assist with the analysis of the results. He has more than a decade of experience in the
development and analysis of computer models and simulations, and performed most of the work
on the prototype evacuation model.
Brian W. Bush, consulting co-investigator and senior mentor, will assist in developing the agentbased model, preparing input data sets, and leading the analysis of the results. He has twenty
years of modeling, simulation, and analysis experience, and was a lead contributor (architect,
developer, and researcher) to TRANSIMS (the Transportation Analysis Simulation System [40]),
a highly detailed agent-based transportation planning model. He will mentor the team in
advanced agent-based modeling techniques and simulation analysis.
Sandy A. Johnson, principal co-investigator, will lead the qualitative fieldwork in New Orleans,
Louisiana, and Galveston, Texas. She will coordinate educational outreach. She is currently a co-
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investigator on an NIH grant for a study on post traumatic stress disorder, resource loss and
hurricane evacuation being conducted in New Orleans.
Brian Muller, principal co-investigator, will lead the policy and utility analysis aspects of the
study and critique the modeling effort. He has more than two decades of experience in policy and
planning analysis. His recent research focuses on use of agent-based models in land use-related
policy simulations and regional transportation planning.
B.
Time Line
Month
1-6
7-9
Key Activities
 Preliminary model development
 Construction of focus group questionnaire
 Calibration and validation data collection
 Supplemental review of literature
 Phase I focus groups
 Incorporation of focus group results into
model
 Model refinement
 Preliminary analysis of model
Milestones
 Preliminary model
 Focus group questionnaire


Focus group report
Revised model





Final version of model
21-24



Phase II focus groups
Incorporation of focus group results into
model
Finalize model
Further analysis of model
Document and disseminate results
Refined model
Draft model analysis section
of project report
Focus group report
Revised model

Project report
23-24

Multidisciplinary seminar

Seminar report
9-13
14-17
18-20


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