AN AGENT-BASED MODEL OF HURRICANE EVACUATION DECISIONS

Seth McGinnis

Institute for the Study of Society & Environment

NationalCenter for Atmospheric Research

Boulder, CO. 80307-3000

ABSTRACT

Despite its importance, the social component of evacuation is usually neglected in planning and modeling hurricane response. We propose to develop an agent-based model of a threatened population’s collective evacuation decision process that takes into account differential response due to demographics, source and flow of information, and social network effects. In addition to improving our understanding of the evacuation problem, this model will serve as an educational tool to aid decision-makers and will build quantitative modeling capability in ISSE.

THE PROBLEM OF HURRICANE EVACUATION

Hurricanes Katrina and Rita demonstrated the tremendous hazard hurricanes present to coastal communities, highlighting evacuation vulnerabilities.Despitethe evacuation of more than one million peoplefrom the area, more than 1100 people died whenKatrina struck New Orleansbecause traditional evacuation planning and policyfailed to prepare for differential evacuation behavior due to social, economic, and psychological reasons. Similarly, when hurricane Rita moved towards the Houston/Galvestonarea, existing evacuation plans were unable to cope with amassiveshadow evacuation spurred by the psychological effects of Katrina, and thousands of evacuees who wouldhave been safer at home were stranded on highways as the stormapproached.

Existing hurricane evacuation models fail to account for social factors that dynamically interact to influenceindividual decision making and drive evacuation behavior.Pastsurveys of evacuations and current evacuation modeling tools areinadequate for studying decision dynamics, the influence of urbandesign, and the importance of demographic variables.This poorlyunderstood individual decision-making process dominates theeffectiveness of evacuations and adequacy of public sectorresponse/support.Much of the past work on evacuationdemand has taken an engineering or economic (utility-theory) approach;we believe that social-scientific theory supports novel, quantitativeexploration of evacuation decision dynamics.

Consensus regarding the importance of quantitative social andbehavioral modeling in evacuation research is growing among keyresearch advisory boards and organizations, including the National Science Board, NOAA’s Science Advisory Board,the Transportation Research Board, the Office of the Federal Coordinator for Meteorology, and the American Geophysical Union.Nevertheless, the currently most sophisticated crisis-behavioralmodels are simply sequential logit models[1] calibrated to predict demandfor hurricane-induced evacuation of cities. Although they can fit observed evacuations using few parametersand there is evidence that models calibrated for one hurricaneare applicable to others, these models lack responsiveness to socialnetworks, public policy, and demographic/geographic detail; they donot distinguish between the decision to evacuate and implementation of that decision.Furthermore, they do not incorporateinformation flow, feedback loops, orcausality indecision-making.

The social aspectof hurricane evacuation also affects the utilityof forecast information: predicted storm track accuracy isimmaterial if the public's evacuation decision does nottake it into account.Our project is an effort to understand this human elementso that it can be incorporated into evacuation policy,planning, research, and modeling.We proposeto develop an agent-based model of a population's evacuation decision process.Simulated citizens in themodel will acquire information about the approaching hurricane anddecide whether and when to evacuate based on their perceptions of safety.This is not an attempt to build a simulation that can accuratelyforecast the collective response, but rather one that can be used toexplore the dynamics of the relevant social systems.We willbe performing a computational experiment to study the collective behavioral response to an extreme weather event.This is an exciting opportunity to add quantitative simulation to ISSE’s expertise in conducting research that integrates human-environment interactions with atmospheric and Earth system dynamics, and will position NCAR well to compete for upcoming NSF multidisciplinary hurricane research programs.

THE BASICS OF AGENT-BASED MODELING

Our approach to this problem uses an agent-based model, or ABM.In mostcomputer simulations, such asGCMs, aphysical system is represented by variables in a set of equations that describe the evolution of state over time.Continuousfields are approximated by an array of variables representing thevalues of the field at discrete points in space.In contrast, an ABM represents the components of thesimulated system directly, as software components with behaviorsmimicking the behavior of the realelement.These components areplaced in a virtual environment and allowed to interact with oneanother.Equation-based modeling works well for systems of continuous fields, while agent-based modeling is well-suited for systems composed of discrete interacting elements.

For example, to model traffic with equations, weconstruct a mesh representing the road network, define a trafficdensity value at each node, and then iterate equations ofmotion to update the traffic density at each timestep.To model it with agents, we instead create software thatrepresents individual vehicles.Each vehicle has attributes likedestination and speed, and behaviors thatdetermine how those attributes are updated: at eachtimestep the vehicle updates its positionbased on its speed,adjusts its speed based on proximity to other vehicles,preferred speed, speed limit, and other factors, and so on.We simulatetraffic by placing a population of vehicles on a virtual road andallowing them to interact according to their behavioral rules.Traffic density is an emergent property of the system inthis representation.

Although human decision-making is too complicated to model generally,infrastructural and temporal constraints that limit the range ofrelevant decisions make modeling itin a limited context tractable. TRANSIMS[2], for instance, clearlydemonstrates that environmental (e.g., road network) and otherconstraints limit the extent and detail of human behavior to a degree that allows simulation.In addition to vehicular traffic, agent-based modeling has also been used successfully to study building evacuation, foot traffic in theme parks and supermarkets, NASDAQ regulations, ISP markets, operational risk factors, and other constrained social decision systems.

We are not attempting to model human thought in our model; rather, we aregiving our agents a simple decision-making process that approximatescrisis-constrained human behavior and studying itsinteractions with the system of information flow in which it isembedded.Our goal is not to accurately forecast the details of anevacuation, but to gain understanding of the structural constraintsimposed by system dynamics so that researchers and decision makers can develop intuition about the consequences of different evacuation policies and methods of information dissemination.

AN AGENT-BASED MODEL OF HURRICANE EVACUATION

In our proposed model, the agents are individual citizens who mustdecide whether and when to evacuate in the face of an approachinghurricane.They make this decision based on information received from the environment.Agents give different weight toinformation basedon the communication channel through which it was received: an agent might pay more attention to evacuation orders than to weather reports, for example.Inaddition, the agents are linked together by a social network (their coworkers, neighbors, and friends) thatprovides them with awareness of decisions made by other agents.These inputs all feed into a decision-making algorithm thatcompares the agent's perceived risk, costs of evacuation, andtime required to evacuate to determine whether to evacuate and if so,when to do so.

The agents have intrinsic attributes that affect their ability andinclination to evacuate, as well as their perception of variousdanger signals.Agents within a subpopulationshare similar attribute profiles; these syntheticdemographics result in different groups responding differentlyto the same signals.For example, one group of agents might have low evacuation costs, high weighting on evacuationorders, and loose social connectivity, similar toaffluent suburbanites who are likely to evacuate early.Anothergroup might have high evacuation costs and low receptivity to danger signals due to language barriers, but high socialconnectivity, much like some immigrant communities.Thissubpopulation's evacuation behavior will differ considerably from thefirst group's and will be strongly dependent on the flow ofinformation through the social network.

The action of the behavioral rules on the internal agent states and attributes can be recast as a set of iterated differential equations. An example of this transformation for our highly-simplified model can be found here:

PRELIMINARY WORK

In conceptualizing this project, we have done preliminary work tolay the foundation for its success. We have so far reviewed database entries for over 1500 articles andreports, scrutinized hundreds of abstracts, and studied hurricaneimpact and assessment reports that evaluate theeffectiveness of evacuations for majorU.S. hurricanes in the last 17 years.Our literature review has alsoprovided us with a preliminary set of variables that we believeinfluence the evacuation decision and given us a sense of thedata available.

We have also implemented a simplified version of themodel described above that demonstrates that the basic dynamic of thismodel produces results that are sensible and qualitatively similarto the evacuation curves presented in the post-storm assessments; see Figure 3, below.

As part of the Memorandum of Understanding between UCAR and Los AlamosNational Laboratory, we have helped to develop a coordinatedproposal entitled "Unified Methodology for Simulating CrisisBehavior"[3] for LANL's Exploratory Research program.There is strongsynergy between this Opportunity Fund proposal, which emphasizesquantitative application of social, cognitive, and behavioral theoryin agent-based simulation, and the LANL proposal, which emphasizes abroader and more computationally intensive simulation paradigm

Figure 3: Observed evacuation curve for Hurricane Opal (l) vs. simulated evacuation curve generated by simplified ABM (r).

PROPOSED WORK

We propose to perform the following work in this project:

We will complete our review of existing research to determine the importantdemographic and other factors to include incommunications-processing and decision-making algorithms. We will pursue collaborations with members of the cognitive psychology community like Kathleen Tierney through our local expert, Mike Page. Our review of communications factors will include existing communications models presented at the Interdepartmental Hurricane Conference that may be incorporated wholesale into the model.

We will extend and elaborate the existing prototype by writing code toprovide support for:

  • a population of agents composed of subpopulations withdifferent characteristic profiles -- that is, class,ethnicity, and other significant demographic subgroups
  • self-perception of hazard as an input to the decision-makingprocess
  • economic and social constraints on the evacuation decision
  • differential effectiveness of communication channels

We will compare data produced by the completed model to data on realhurricane evacuations to evaluate its performance.We will also perform sensitivity analysis using variance-based methods (Latin hypercube and combined orthogonal arraysampling) to gain insight into the dynamics of the evacuation system.

RESEARCH OUTCOMES AND DELIVERABLES

Social behavior has not yet quantitatively been accounted for in hurricane evacuationmodeling, an oversight that contributes to loss of life.Thisresearch will begin to correct that oversight and bridge the gapbetween physical and social sciences, providing a tool that translatesweather and climate science results like hurricane track predictionsinto a decision support product with meaning and value to policy makers, publicplanners, and laypeople confronted with the issue of hurricaneevacuation. This project will generate the following knowledge products:

  • An agent-based simulation model of evacuation behavior that interfacessocial systems with climate/weather systems, paving the waymethodologically for future modeling efforts to integrate humanbehavior with the earth-sun-climate system.This project canact asthe vanguard for one aspect of the Integrated Hurricane Program envisioned by Greg Holland, Jeff Lazo, and others.
  • A sensitivity analysis identifying the importance of various demographic, informational, infrastructural, and policy variables that affect hurricane evacuation response.
  • A catalog of recommendations for the behavioral survey questions mostrelevant to reducing uncertainties in evacuation behavior models.
  • A computer model that is also usable in a facilitated workshop as an educational tool to aid in the development ofdecision-makers' intuition regarding mass evacuation behavior.

BROADER IMPACTS OF THIS RESEARCH

This research integrates research and education by exploring theimpact of weather science results on public decisions.In addition,our model will be simple enough to be suitable for use by educatorsand informed laypeople.We will broaden the participation of under-represented groups in UCARresearch on the impacts side by explicitly accounting forsocio-economic and demographic diversity in studying the effects ofhurricanes on populations.

This work enhances UCAR's infrastructure for research and education bybuilding a strong modeling collaboration with LANL, and by buildingthe capacity for ISSE to engage in quantitative simulation of socialsystems and the broad modeling of weather- and climate-related human activities. It will connect with the NCAR-wide Integrated Hurricane Project, contributing social modeling capabilities to that effort.

This project will require no GAUs.Results of this work will be disseminated through workshops and publicavailability of the model for download from the Web.This project benefits society at large by enhancing our ability tostage successful hurricane evacuations.

This project will provide policy-makers with a tool that elucidatesthe impact that policy choices have on the populace.Products of thisresearch will help policy-makers to better understand how their uniquecommunities can mitigate potential human impacts of hurricanes byproviding nuanced models of evacuation scenarios.In particular,results of this research will aid:

  • Transportation-planning-oriented evacuation models, which benefitfrom more detailed demand models;eventually, behavioralsimulations can be linked directly to traffic models
  • The design of surveys that elicit information about criticallyinfluential behaviors
  • Urban planning initiatives, by allowing them to better account forbehavioral dynamics in evacuation.
  • Public information dissemination, particularly to demographic groupsat high risk.

Our collaborators and university interactions include: Brian Bush,Los Alamos National Lab, will contribute agent-based modeling, simulation, and statistics expertise; Brian Muller, CU-Denver School of Planning, will contribute analysis of urban design factors; Sandy Johnson, LSU School of Public Health, will contribute demographic vulnerability information. Kathleen Tierney, NationalHazardCenter at CU-Boulder, has also expressed interest in this project.

REFERENCES

R. Axelrod and L. Tesfatsion, "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences", in L. Tesfatsion and K.L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Elsevier, 2006;

E. Bonabeau, “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems,” PNAS 99, Supplement. 3: 7280-7287, 2002;

E. Chin-Ping Chang, “Traffic Simulation for Effective Emergency Evacuation”, Oak Ridge National Laboratory, 2003;

H. Fu and C.G. Wilmot, "Survival Analysis Based Dynamic Travel Demand Models for Hurricane Evacuation," Proc. Transportation Rsch. Board 85th Ann’l Mtg., Washington, D.C., 20-26 January 2006.

C.M. Macal and M.J. North, “Tutorial on Agent-Based Modeling and Simulation”, Proceedings of the 2005 Winter Simulation Conference;

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[1]These are essentially linear statistical (regression) models wherethe dependent variable is the logistic function of the probabilitythat a household decides to evacuate in a given time interval;independent variables typically relate to the households, thephysical environment, and public information/orders.

[2] TRANSIMS, from Los Alamos National Labs, is a traffic simulation system capable of simulating the second-by-second movements of every person and vehicle through the transportation network of a large metropolitan area. See and for details on the TRANSIMS project. A simple demonstration of some of the principles involved is available at

[3]See a copy of the proposal.