Increasing Evacuation Communication Through ICTs: An Agent-based Model Demonstrating Evacuation Practices and the Resulting Traffic Congestion in the Rush to the Road

Homeland Security & Emergency Management 2015; 12(3): 497–528

Keri K. Stephens*, Ehsan Jafari, Stephen Boyles, Jessica L. Ford and Yaguang ZhuIncreasing Evacuation Communication Through ICTs: An Agent-based Model Demonstrating Evacuation Practices and the Resulting Traffic Congestion in the Rush to the RoadDOI 10.1515/jhsem-2014-0075

Abstract: Understanding evacuation practices and outcomes helps crisis and disaster personnel plan, manage, and rebuild during disasters. Yet the recent expansion in the number of information and communication technologies (ICTs) available to individuals and organizations has changed the speed and reach of evacuation-related messages. This study explores ICTs’ influences on evacuation decision-making and traffic congestion. Drawing from both social science and transportation science, we develop a model representative of individual decision making outcomes based on the amount of ICT use, evacuation sources, and the degree of evacuation urgency. We compare the evacuation responses when indi-viduals receive both advance notice of evacuation (ANE) and urgent evacuation (UE) messages under conditions of no ICTs and prolific ICT use. Our findings from the scenarios when there is widespread ICT use reveal a shift in the evacuation time-scale, resulting in traffic congestion early in the evacuation cycle. The effects of this congestion in urgent situations are significantly worse than traffic conges-tion in the advance notice condition. Even under conditions where face-to-face communication is the only option, evacuations still occur, but at a slower rate, and there are virtually no traffic congestion issues. Our discussion elaborates on the theoretical contributions and focuses on how ICTs have changed evacuation

*Corresponding author: Keri K. Stephens, Associate Professor Organizational Communication and Technology, Department of Communication Studies, Moody College of Communication, 2504A Whitis Ave., Stop A1105, Austin, TX 78712, e-mail: [email protected] Jafari and Stephen Boyles: Cockrell School of Engineering, The University of Texas at Austin, USAJessica L. Ford and Yaguang Zhu: Moody College of Communication, The University of Texas at Austin, USA

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498 Keri K. Stephens et al.

behavior. Future research is needed to explore how to compensate for the rush to the road.

Keywords: communication; crisis; disaster; evacuation; ICTs; transportation.

1 IntroductionCommunication practices during emergencies have changed considerably in the past 5 years. With the proliferation of Internet based social media technologies (e.g. Facebook, Reddit, and Twitter), and the prominent use of mobile devices, people have many options for receiving information and even participating in disaster-related conversations (Pechta et al. 2010; Latonero and Shklovski 2011; Veil et al. 2011; Stephens et al. 2013, 2014a,b; Sutton et al. 2014). The public is no longer reliant on official emergency information sources to disseminate urgent information because individuals can function as citizen journalists (Sutton 2010). Disaster information is now connected through a technology-mediated informa-tion flow that includes relationships between the public, government agencies, and the media (Pechta et al. 2010). Yet rarely does our research couple the use of these information and communication technologies (ICTs) in disasters, with the infrastructure needs to safely evacuate people (Murray-Tuite and Wolshon 2013). Our research begins to address this gap by jointly modeling both the demand (i.e. individual agent needs) and supply (i.e. transportation network) sides of these evacuation practices. Our study focuses on the influence of combinations of ICTs on evacuation decisions and the subsequent travel time to reach a safe location.

Disaster warning processes are complex and involve both social systems – e.g. message receivers, social networks, and warning sources – and infrastruc-ture issues like transportation capacity. With the exception of hurricane research, most of the scholarship on warnings and evacuations was conducted prior to the mid-1990s (Sorensen and Sorensen 2007). Literature considering both the supply and demand side of transportation during evacuations is scant. Additionally, previous warning and evacuation research makes several broad assumptions and relies on older theories of persuasion that need to be revised as we incor-porate transportation concerns into our purview. First, top-down models of offi-cial warning practices are not representative of how people actually learn about disasters today (Sorensen and Sorensen 2007; Sutton et al. 2008). The use of mobile devices like cell phones, have made it possible for people to have contin-ual access to multiple communication channels (Stephens et al. 2013; Stephens and Barrett 2014).



Increasing Evacuation Communication Through ICTs 499

While many researchers have explored and modeled evacuation behavior (e.g. Whitehead et al. 2000; Gladwin et al. 2001; Lindell et al. 2005; Hasan et al. 2011), none of these researchers have focused specifically on ICTs and commu-nication channels. Previous researchers have complied comprehensive lists of the factors associated with evacuation decisions (e.g. Murray-Tuite and Wolshon 2013) which are helpful in specifying the numerous variables that play a role in why people choose to evacuate or not. The current research focuses less on the micro reasons for evacuation decision-making and concentrates primarily on the role ICTs play in evacuation decisions and the subsequent traffic congestion. Our research supplements recent projects that merge social science with transpor-tation engineering (Ren et al. 2009; Hasan and Ukkusuri 2011; Russo and Chilà 2014) providing a supply and demand perspective on our evacuation lifeline: roads. Beyond addressing research calls, this study has direct implications for emergency managers and evacuation forecasters.

Dash and Gladwin (2007) specifically note that we need more research focused on how the information age changes decision-making, the timing of those decisions, and the diffusion of information. To accomplish these objectives, we first review the literature on ICTs and determine theoretically meaningful ways to group ICTs relevant in evacuations. Next, we discuss three primary sources of evacuation information, decision-making, and infrastructure considerations. Using the concepts from this literature, we subsequently define the behavioral rules and variables included in our simulation models using the Grimm et al. (2006, 2010) framework. Our simulation results show the differences between evacuations in urgent and advance notice situations when ICTs are heavily used and when the only ICT is face-to-face communication. Our discussion focuses on the implications of these findings and outlines directions for additional work in this interdisciplinary area.

2 Background and Literature Review

2.1 Information Theory

Regardless of how people are notified that they need to evacuate, the evacua-tion decision-making process is complex. In their study of message diffusion on a college campus, Egnoto et al. (2013) found that people actively look to confirm emergency information, and most often, that confirmation is from other people. In a disaster, a common way to validate the legitimacy of an event is to send mul-tiple messages (Mileti and Sorensen 1990). This message repetition, or redun-




dancy is a fundamental part of information theory (Shannon and Weaver 1963), a theory recently used to examine emergency alert practices and ICTs (Stephens et al. 2013). In their study, Stephens and colleagues (2013) grouped the 12 ICTs they studied into two categories that were based on how quickly ICTs allow mes-sages to reach intended audiences. For example, some ICTs, like text-messaging are available on mobile devices and reach their intended recipients more quickly than having to reach diverse contacts face-to-face.

Information theory provides the theoretical framework for this study because it combines decision-making, imperfect transmission of information, and the use of communication channels – ICTs – to explain how we reach people with infor-mation. Information theory is an interdisciplinary theory that has been applied widely to disciplines like information science, computer science, and communi-cation studies. The portion of the theory we use in this study focuses on informa-tion transmission principles and an understanding of how different ICTs are used to reach audiences with urgent information.

2.2 Defining ICTs Used for Information Dissemination

Prior research demonstrates that communication channels (ICTs), sources, and message content impact evacuation rate and speed (Lindell and Perry 2012). Our study focuses on the influence of these combinations of ICTs on evacuation deci-sions and the subsequent travel time to reach a safe location. Given the many types of ICTs used to disseminate disaster information, we chose to focus on mediated forms like text messages through mobile devices, public social media like Twitter, news media such as television, and face-to-face communication. It is also important to note that while individuals can use mediated ICTs, so can work and news organizations.

We call the first category of ICTs used for disaster communication, inter-personal mediated because these messages are shared between interpersonal contacts through devices that mediate the communication. Mobile devices are a specific form of interpersonal mediated ICT, since people use them for text messaging, phone calls, and email (Stephens et al. 2013, 2014a,b). Interpersonal mediated communication is critical in disasters (Spence et al. 2011), because the relationships we establish prior to a disaster typically have the most influence over our response to these events (Mileti and Peek 2000).

Public social media platforms like Twitter (a 140 character/message system) are now used to share life-saving information in disasters (Starbird and Palen 2010; Sutton 2010). While some studies suggest that information shared through social media such as Twitter can provide more timely and more accurate




information than official sources (e.g. Sutton et al. 2008), it can also be the source of rumors and inaccurate information.

Yet mediated ICTs, like mobile devices and public social media, are not the exclusive answer to improving evacuations. Some people cannot afford these technologies while others lack the digital literacy skills required to use all of their device functions (Murray-Tuite and Wolshon 2013). In addition, it is still common that certain emergencies completely destroy communication networks and limit access to communication technologies like the Internet (Mileti and Peek 2000; Manoj and Baker 2007). Even in emergencies where ICTs are fully functional, research suggests that face-to-face communication still plays an important role in evacuations. This ranges from using uniformed officers to help evacuate people (Arlikatti et al. 2006) to integrating face-to-face communication in communi-ties who place high trust in their interpersonal connections (Spence et al. 2011). Hasan and colleagues’ (2011) research on hurricane evacuations found that “a household who first hears about the evacuation notice from a friend or relative instead of any other source (e.g. television, radio, Internet) has a higher prob-ability to evacuate” (346). Their findings support earlier work by Gladwin and Peacock (1997) and Lindell et al. (2005) who also found that receiving evacuation information from interpersonal sources or authorities increases the evacuation likelihood more than receiving information from the news media.

Traditional media like television, newspapers, and radio, are often considered mass media because they can communicate with broad publics (Pechta et al. 2010). These traditional ICTs are still important information dissemination chan-nels during emergencies (Sutton et al. 2008), although they are changing consid-erably. Today, media consumers are constantly moving, “they are time shifting, format shifting, and screen-shifting” (Oswald and Packer 2012: p. 279). For example, people watch TV on their mobile phones, search the internet on their TVs, and listen to radio stations through the internet connection they have with their computer. Despite these merging ICT formats, many news organizations still provide people disaster information through traditional channels like television and radio, and thus even people who do not have regular internet access might have access to these traditional ICTs.

2.3 Sources that Share Emergency Information

Decades of research suggest that in an emergency or disaster, people are “infor-mation hungry” (Mileti and Peek 2000: p. 191) because they want to reduce their uncertainty associated with the situation (Heath and Gay 1997). Common cog-nitive strategies to reduce uncertainty include seeking, confirming, and sharing




information (Heath and Gay 1997). Here we discuss three sources of disaster mes-sages: individuals, organizations, and news organizations.

Individuals can reach their interpersonal contacts more quickly through ICTs like text messages, group text messages, and phone calls, than if they needed to find each contact in person. As people communicate with different sources through multiple ICTs, interpersonal communication – including using ICTs – plays an important role in helping people make sense of disaster information (Spence et al. 2011; Stephens et al. 2013). When studying shelter-in-place mes-sages, Stephens and colleagues (2013) found that asking people to go tell (i.e. find, call or text) someone they know about the emergency heightened people’s sense of urgency more quickly than simply relying on messages delivered through ICTs from a non-interpersonal source, like an organization.

Organizations can also share information concerning emergencies. Organiza-tions can be government agencies, employing organizations, non-profit groups, or any form of non-governmental organizations (NGOs). In a disaster, these organizations have two primary ways to communicate with their stakeholders: intra-organizational mediated communication and public forms of communica-tion. First, organizations have intra-organizational contacts – e.g. employees, contractors, stakeholders – who can be reached through mediated means such as email distribution lists. In their study of a university campus emergency alert, Stephens et al. (2013) found that social media played almost no role in the early notification process because the stakeholders received emails and text messages sent by the organization. However, some organizations use public mediated com-munication, like social media and automated text-messaging broadcast systems, to notify people of an emergency. For instance, during the Hurricane Sandy evacuation, the New York City government sent text messages to warn residents. Although only 22% of the people in their survey actually received at least one text message, 82% of those receiving the messages said they found the texts either very useful or somewhat useful (Gibbs and Holloway 2013). In studies examining re-tweeting, or passing on information, it appears that repeating and sharing the information serves to amplify the message and extends an organization’s reach when disseminating disaster information (Starbird and Palen 2010).

News organizations are also still playing a key role in disaster notifications. Research on wildfires in California suggests that while respondents relied on national and local news networks for disaster information, they felt it was biased toward metropolitan areas, and was prone to sensationalism (Sutton et al. 2008). Despite these sentiments, news organizations still function as a main source of information for individuals in an emergency. To combat the increased number of media outlets available, news organizations now use a mix of ICTs to deliver their messages. News organizations, can no longer be associated tightly with TV




or radio because they are increasingly using social media to disseminate their messages (Oswald and Packer 2012).

2.4 Evacuation Decisions

Notifying people about the need to evacuate can fall into two main categories: immediate – e.g. tornado, wildfire, or flood – and drawn out – e.g. hurricane (Tierney et al. 2001; Sutton 2010). In their study of hurricanes, Whitehead et al. (2000) determined that storm intensity is a key determinant in household evacuation decisions. In their survey after Hurricane Sandy, Gibbs and Holloway (2013) found that information on the severity of the storm contributed twice as much influence over advice from friends, relatives or the media, in peoples’ decisions to evacuate. During an emergency requiring evacuation, people go through a five-step process of hearing, understanding, believing, personalizing, deciding, and performing (Mileti and Peek 2000), with that final step being the actual evacuation. The time required to move through these stages differs from person to person. However, the amount of messages an individual receives about an emergency may alter their evacuation decision making; thus increasing their evacuation speed and their subsequent rush to get onto the road.

2.5 Infrastructure Considerations and Evacuation Decisions

Thus far, much of our review has focused on communication and information dissemination outcomes in disaster evacuations. Next, we link ICT use with infrastructure issues, specifically transportation needs during evacuations. Abdelgawad and Abdulhai (2009) provide a list of supply-side and demand-side evacuation strategies used to help overcome the severe congestion that can occur during life-threatening events. Common supply-side strategies include contra-flow operations – often used in hurricanes, expansion of traffic lanes, and the use of traffic signals to support increased demand on infrastructure.

Managing the demand-side of evacuations is also a valid strategy, but one that has received less attention than the supply-side (Murray-Tuite and Wolshon 2013). Demand-side strategies include actions like reducing background traffic – keeping people off the road unless absolutely necessary, and staged or sequenced evacua-tions. One of the key contributions made by modeling network traffic processes is an understanding of departure patterns (Levin et al. 2015). Managing the demand-side of an evacuation implies that we can control the behavior of the actors in a given evacuation area. The nuances of individual actor decisions are difficult




to control, but recent research has focused on economic subsidies (Gardner et al. 2011), such as toll roads, as a way to incentivize and change traffic patterns.

3 Simulation ModelIn disasters, it is vital for emergency personnel to better understand departure patterns. Additionally, personnel responsible for disseminating evacuation mes-sages will benefit by having a better grasp on how actors in an evacuation areas respond to these messages. Ultimately, people’s decisions to evacuate are driven by the information they receive through ICTs and from various sources, such as interpersonal, face-to-face communication. While it is logical that providing more information about an emergency will accelerate departure patterns, emer-gency planners need tools to help them understand what influences these depar-tures and the patterns of departures over time. This research begins to respond to these needs by developing a formal model for evacuation practices using an agent-based computer simulation.

We first provide an overview of the model and then discuss its components in more detail. Our model description follows the overview, design concepts, and details (ODD) protocol for describing individual and agent-based models (Grimm et al. 2006, 2010).

3.1 Model Purpose

Agent-based models (ABM) are particularly well suited to focus on the behav-iors of adaptive actors who comprise a social system and whose communication behavior can influence others (Macy and Willer 2002). Santos and Aguirre (2004) stress that simulation models can be very helpful to understand disaster evacu-ations, but they have historically ignored human behavior and assumed people were passive receptors of evacuation notifications. To overcome some of these limitations, the purpose of our model is to integrate the supply and demand com-ponents of evacuation behavior, thus more completely helping disaster person-nel understand how human behavior influences evacuation practices.

3.2 Entities, State Variables, and Scales

This ABM model is developed using information theory and the previous review of literature. The model implemented in this research has three entities: individuals,




organizations, and news media and some core design concepts; which we define and explain in the subsequent section.

3.2.1 Individuals

Individuals are members of each household. Our model assumes that all household members decide and act together; thus, they are modeled as one individual agent. The behavior of each individual in response to an evacuation notice is modeled by the sensitivity factor. This factor shows how sensitive a person is in responding to evacuation notices. A low sensitivity factor means that the agent is likely to evacute very late whereas an individual with a high sensitivity factor will evacuate quickly. The sensitivity factor is allowed to vary across the individuals in a population, but during the simulation period, it is fixed for each individual. In addition to these static sensitivity factors, agents are also associated with a dynamic factor, referred to as effective impact, which indicates the current state of individuals. In this ABM agents can have two states at each time period: “evacuating” or “waiting.”

3.2.2 Organizations

Organizations can also share information concerning emergencies. Organiza-tions can be government agencies, employing organizations, non-profit groups, or any form of non-governmental organizations (NGOs). In a disaster, these organizations have two primary ways to communicate with individuals: intra-organizational mediated communication – e.g. employees, contractors, stake-holders – and public forms of communication, like social media and automated text-messaging broadcast systems that notify people of an emergency.

3.2.3 News Media

News organizations still play a key role in disaster notifications. News media can reach some people, regardless of whether they have ICTs and regardless of the urgency of the situation.

3.2.4 Design Concepts

The interaction between agents is modeled through the communication links. The messages received through connection links have different impacts based




on the source of the information. For example, an evacuation message received from a family member is more trusted compared to a message received through news or organization channels. After receiving new information, agents have an updated impact factor created by adding the new information. The state of the agent will change if the updated effective impact is higher than that agent’s sen-sitivity factor. Also the model assumes that evacuated agents still have limited access to communication technologies (e.g. phone, text, Twitter, Facebook, etc.) to share their latest information with their connections.

3.3 Process Overview and Scheduling

Our model assumes that individuals receive information from organizations and news media, hence these entities are connected via one-way links. In addition, individuals can communicate with other individuals using a two-way channel. Each individual can have face-to-face (FtF) connections, mediated interper-sonal connections (e.g. phone), and some mediated public connections (e.g. social media) with other individuals. These between-individual connection links are used for both sending and receiving the evacuation information. Figure 1 describes the connection links between three different entities of the ABS model constructed in this study.

The model is a discrete time model where the events happen at time steps. Individuals, organizations, and news, however, have different time steps. In case of organizations and news, a time step simply means the time point at which these agencies send information to their connections regarding the need for an evacuation. In addition to sending information at each time step, individuals evaluate that information and update their evacuation state at each time step. When the effective impact of the message on individual agents becomes higher than the individuals’ sensitivity factors, individuals update their state to “evacu-ating” and will leave their home within 30 min, otherwise individual agents will wait for more information.

The series of events at each time step are schematized in Figure 2. In this pseudocode, Δtn, Δto, Δti stand for time steps associated with news, organizations, and individuals, and t mod ΔtX returns the remainder of division of t by ΔtX.

If the current time step matches the time step considered for each model entity, then that entity sends an evacuation notice to a selection of their contacts. At the end, individuals update their effective impact by adding the impact of the new received messages, and changing their state to “evacuating” if the updated effective impact is higher than the sensitvity factor.




3.3.1 Sensing

The variables sensed by individual agents are evacuation notifications received from other individuals, work organizations, and news organizations.

News Organizations

Individuali

Interpersonal(phone)

Public(facebook)

FtF

Other individuals

Figure 1: Structure of Connection Links Between Different Agents.

Time step

if send info to selected individuals end if if send info to selected individuals end if if send info to selected individuals end if

for all Individuals update effective impact update state

end for

end of time step

Figure 2: The Process at Each Time Step.




3.3.2 Interaction

The communication links between individuals, work organizations, news organi-zations, and other individual agents are used to model the interactions between different agents.

3.3.3 Stochasticity

The number of FtF, mediated interpersonal, and mediated public connections varies across individuals and are random numbers drawn from a uniform distri-bution. Although the portion of the population assigned to receive information from organizations and news media is a fixed number, the individuals receiving these types of messages are randomly selected.

3.4 Initialization

Our model assumes that 10% of the individuals are aware of the evacuation notice at the beginning of simulation and participate in sending information to other indi-viduals along with organizations and news. As time progresses, more individuals receive updates about the event, which increases the evacuation process. Also, we assume that some portion of the individuals, 10%, never participate in evacuation process; they do not spread information to other individual agents and will not evacuate. This assumption is derived from the literature reviewed on evacuations.

3.5 Input Data

The model does not use numerical input data to represent time-varying processes, thus the model parameters are derived from prior published empirical and theo-retical research. In addition, our research team has complete data on traffic pat-terns and evacuation routes in this area, which limits the assumptions needed in the simulation and constitute the only numerical observations used. We used data about each traffic analysis zone from The Network Modeling Center located within the Center for Transportation Research at the University of Texas at Austin.

3.6 Submodels

Two evacuation scenarios are studied: “advance warning” and “urgent” evacua-tions. People have advance warnings during some types of disasters – e.g. hurri-




canes, – while other disasters happen with little or no notice – e.g. wildfires. The urgency of an evacuation request likely affects people’s decisions to evacuate; therefore, we model two different scenarios: advance warning and urgent. Not only are both evacuation contexts important, but juxtaposing these situations provides a clearer picture of how the level of urgency changes information dis-semination and evacuation practices.

The impact of ICTs on evacuation process and traffic network congestion is evaluated by simulating each evacuation scenarios for two alternatives where ICTs are readily available and those where only face-to-face and news, or non-ICT-mediated communications are used to share evacuation information. The details of parameter values for agents and each scenario are summarized in Table 1.

3.6.1 News and Organizations

In these models, we assume that both types of organizations receive evacuation notices from officials and will release information at their designmated time steps. The messages, however, do not reach all their connections and a certain portion of the individuals with news and organizational connections will receive updates. This assumption is derived from an understanding of human behavior and work patterns because some people are not reachable after work hours and some people do not watch or listen to the news.

3.6.2 Individuals

Individuals play the role of both a receiver and a sender of emergency evacuation information. In addition to receiving information from other individuals, work organizations, and news organizations, each person participates in information sharing updated information to other individuals. The impact of each message received by individuals is added to their effective impact, and a change in their state from “waiting” to “evacuating” is updated if the effective impact is higher than the sensitivity factor.

( )( ) ( 1)

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No IC

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With

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(Sce

nario

2)

No IC

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cena

rio 3

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(Sce

nario

4)

Agen

ts

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onne

ctio

ns

Rand

om {0

, 10}

Ra

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{0, 1

0}

Rand

om {0

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Ra

ndom

{0, 1

0}

Med

iate

d in

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erso

nal

–

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om {0

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Ra

ndom

{0, 2

0}

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Ra

ndom

{0, 5

0}

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Rand

om {0

, 50}

Th

resh

old

valu

e

Rand

om {1

0, 5

00}

Ra

ndom

{10,

500

}

Rand

om {1

, 50}

Ra

ndom

{1, 5

0}

Tim

e st

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r sen

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in

form

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5 m

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134,

896

13

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6

134,

896

Si

mul

atio

n ho

rizon

8

h

8 h

5

h

5 h

Tabl

e 1

(con

tinue

d)




(where ei(t) is effective impact of individual i at the end on time step t, Ij is the impact of message j, and Ri(t) is the set of messages received by individual i during time step t. si(t) is the state of individual i at the end of time step t and sFi is the associated sensitivity factor).

As long as individuals are in a waiting state, they receive and send informa-tion. When individuals change their state to evacuating, they will leave their home within 30 min and take the path with the least travel time to the closest exit point from their home. The behavior of individuals is tracked until they reach the exit point in the road network and after that point we assume that enough resources are available for them to continue the evacuation process.

3.6.3 Traffic Model

This simulation models traffic flow and congestion based on the cell trans-mission model developed by Daganzo (1994, 1995) as a discrete approxima-tion to the hydrodynamic model of Lighthill and Whitham (1955) and Richards (1956). This model describes queue and congestion formation at bottlenecks by limiting traffic flow to the roadway capacity, and to the physical space on the roadway. The cell transmission model accounts for the “spillback” phenom-enon, where vehicles, unable to enter a full link, block upstream vehicles from moving. This latter phenomenon is a common trigger for severe congestion in urban areas.

3.6.4 Network Model

The simulation environment consists of 134,896 individual agents, each rep-resenting a household in Williamson County, Texas. The simulation environ-ment also includes the physical roadway network, and the social networks that connect households and organizations. The roadway network represents all major and minor arterials, highways, and freeways in Williamson County, and consists of 2617 roadway links and 1281 intersection nodes. According to common practice in transportation planning, Williamson County is divided into 199 geographic zones based on geography and demography and. One of the central assumptions in all transportation systems is that all activity is concen-trated at the zone centroid, and centroid connectors are used to define the entry (exit) points of trips into (out of) the roadway network (Jafari et al. 2015). A speci-fied time horizon of 5 and 8 h is simulated for urgent and advance warning sce-narios respectively. Evacuations during urgent events will happen more quickly




and thus, we ended the analysis at 5 h; a time when our model indicated that travel slowed considerably.

3.6.5 Evacuation Modeling

The simulations represent a case where a disaster occurs at the southern edge of Williamson County (the edge nearest central Austin, Texas), and households evacuating must leave towards the north, east, or west. The location of this event is representative of a disaster affecting the Austin metropolitan area, where house-holds must evacuate away from the central city. Households are free to evacuate on any route as long as they leave the county in one of these directions. In the simulation, we assume that in the absence of congestion, evacuating households will leave via the fastest route. In contrast to some planning models, our simula-tion does not assume that drivers can correctly anticipate network congestion, because during emergency evacuation these situations are constantly chang-ing. Therefore, the assumption that a driver’s experience would allow him/her to anticipate congestion would not be justified. Since there are many potential exit points, our model assumes that agents are rational and select the closest exit point to their home. A map of this area, along with exit points indicating where it is safe to evacuate is found in Figure 3.

Together, these components create a model that integrates information spread with the consequences of traffic congestion on the physical roadway network.

4 Model Parameters

4.1 Evacuation Decision Threshold

Agents will vary at the point in time when they decide to evacuate. For the advance warning scenarios, we used a random number between 10 and 500 to determine this decision point. For urgent situations, people are much more likely to evacu-ate and do so quickly (Whitehead et al. 2000). Our simulation parameter was set to a random number between 1 and 50 for this scenario. Regardless of condition, research suggests that not everyone will evacuate regardless of the event severity. For our simulation, we set the maximum evacuation rate at 10% for both simula-tion conditions.




4.2 ICTs and Disaster Information Sources

4.2.1 Individuals

In all scenarios, each agent has some FtF connections that are determined by using a random number between 0 and 10. In scenarios with ICTs, each agent also has some mediated interpersonal connections represented by a random number between 0 and 20, and some mediated public connections that are deter-mined using a random number between 0 and 50. The range of random numbers was derived from literature based on the likelihood and reach of people through

Figure 3: Williamson County Network with Exit Points as Safe Evacuation Points. Circles show the concentration of individual agents at different parts of the network (a larger circle indicates a higher population). One can observe that a high portion of the individuals are living in the southern area, especially south west.




each channel. In all simulation scenarios, each individual communicates with a number of his/her connections which are selected randomly.

4.2.2 Organizations

During an evacuation, organizations can also communicate with their employees and members, especially through ICTs. In some situations, like work organiza-tions, these groups have complete membership communication rosters so our models assume that communication attempts via intra-organizational channels reach 30% of the total agents at each time period. Organizations can also use mass media to communicate with their members and in our model, public organi-zational broadcasts reach 50% of the agents at each time period.

4.2.3 News Media

Finally, news media can reach some agents, regardless of whether they have ICTs and regardless of the urgency of the situation. Our simulation assumes that news media reach 80% of the agents initially. In advance warning situations, we assume that news sends an update every hour and it reaches 50% of the agents at each time period. In urgent situations, news updates are every 15 min and they reach 50% of the agents at each time period.

4.2.4 Information Sharing

In all simulation scenarios, we assume that information can be shared with con-tacts every 5 min. At every time period, the likelihood of sharing information varies depending on whether the event is urgent or not. In an urgent situation, our simulation assumes that 50% of the agents will send information to 50% of their connections. Considering the abundance of available ICT contacts, this includes all of an agent’s communication media. In the no ICT condition, each agent is limited to his or her FtF connections. In an advance evacuation notice situation, our simulation assumes that 20% of the agents will send information to 20% of their connections.

Organizational information sharing is less frequent than individual-level sharing. In our simulation, we assume that organizations will send additional information at 1 h intervals in an advance notice and in 15 min intervals in an urgent situation.




4.3 Design of Simulation Experiments

This study focused on the impact ICTs have on evacuation practices. To under-stand this influence, we created four simulations that manipulated the presence/absence of ICTs and the severity of the event (i.e. urgent or advance notice). The experiments were conducted by varying some parameters mentioned in our model design, and fixing other simulation parameters. These parameters are found in Table 1.

Each simulation run had 134,896 agents (m = 134,896). The simulation time was set to 8 h for the advance notice situation and 5 h for the urgent situation.

4.4 Evacuation Outcomes

We are particularly interested in two types of outcomes from the simulation. The first type concerns how many agents evacuate during the disaster. The second outcome is the time spent in a vehicle to reach a common evacuation point, or location that was considered safe. If there is no congestion, it is possible for an agent in this simulation to evacuate in 15 min or less. Since we included conges-tion in our models, this outcome offers an integrative perspective on both the behavioral and infrastructure realities.

5 Simulation FindingsThe model was implemented in Java.1 The average computation times were 20 min for each scenario and the models were run on an Intel X5677 processor running at 3.47 GHz. To better examine the range of variation generated by the model, we ran a sensitivity analysis consisting of 100 runs for each scenario. The simula-tion results are stable and independent of the randomness found in the simula-tion. As long as the parameters introduced in Table 1 are fixed, it does not matter which agents are selected to have organizational channels and which agents are selected to receive and send information. Figure 4 shows the boxplots for 100 simulation runs under each scenario. Each boxplot consists of a box, which goes from the first quartile (left edge) to the third quartile (right edge), and two hori-zontal lines, called whiskers. The first whisker extends from the first quartile to

1 The source code is available at http://tinyurl.com/stephendboyles/Evacuation.java



http://tinyurl.com/stephendboyles/Evacuation.java


the minimum value and the second whisker extends from the third quartile to the maximum value. The vertical line within the box indicates the median value.

Table 2 represents an overview of our findings concerning the number of agents who evacuated in the urgent and advance notice condition. Despite sto-chasticity of the model, there is no meaningful deviation between the reported values in Table 2 and values reported in Figure 4.

5.1 Graphical Analysis with Outcome of Evacuation Departure Time

In the advance notice scenario, agents evacuate at a fairly uniform rate over an 8 h time period regardless of whether there are ICTs used in the notification or

A

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Figure 4: Boxplots Showing Variation in Size of Evacuated Population for 100 Simulation Runs.(A) Advance Notice of Evacuation. (B) Urgent Evacuation.

Table 2: Evacuation Outcomes Averaged over 100 Simulation Runs.

ScenariosResults

Advance Notice of Evacuation

Urgent Evacuation

No ICT (scenario 1) With ICT (scenario 2) No ICT (scenario 3) With ICT (scenario 4)

Evacuated 30,448 (22%) 65,193 (48%) 96,661 (71%) 109,247 (81%)Notified 118,906 (88%) 121,406 (90%) 118,784 (88%) 121,407 (90%)




not. However, when ICTs are present, more agents evacuate overall, n = 64,369 (48%) as compared to n = 30,534 (23%) for the non-ICT condition. In addition, agents evacuate more quickly with ICTs than without ICTs. Figure 5 provides a graphical depiction of these data. The x-axis shows the time elapsed from the beginning of the simulation.

In the urgent evacuation scenario, evacuation practices exhibit a striking dif-ference from the advance notice situation. This sense of urgency is apparent in Figure 6 because regardless of whether ICTs are used or not, most agents evacu-ated within 90 min (1.5 h). Although this rapid evacuation finding is what prior research predicts, our findings provide unique insight into ICT use. Of those who use ICTs, over 75% of the agents who will evacuate do so within 30 min (1800 s). This is a much faster evacuation rate than seen without ICTs; in this case the evacuation surge is spread over 1–2 h instead of the immediate 30-min depar-ture. In an area like Williamson County, Texas, this makes a significant impact on traffic congestion.

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Figure 5: Departure Times for Advance Notice Evacuation Scenarios with ICTs and without ICTs.

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Figure 6: Departure Times for Urgent Evacuation Scenarios with ICT and without ICT.




5.2 Graphical Analysis with Outcome of Travel Time (a measure of congestion)

Travel time, a measure of congestion as well as time on the road to reach the evacuation point, is once again quite similar between the use of ICTs and no ICTs when there is advance notice given for the evacuation. This is likely due to uniform evacuation rates and the fact that fewer agents evacuate immediately in advance notice conditions than do in urgent situations. This evacuation rate is depicted in Figure 7. Note that time is expressed in minutes in this graph.

The travel times in the urgent evacuation scenario are very different than in the advance notice scenario, regardless of whether there are ICTs or not. The dis-tribution of travel times is skewed heavily to the right, indicating that a number of travelers face extremely long travel times due to congestion. In particular, the travel times are significantly longer where there are ICTs used as opposed to con-ditions where no ICTs are used. According to the simulation, some agents waited over 3 h to reach their final destination. Note that this increase in congestion sup-ports our claim that faster information dissemination does not necessarily lead to a more efficient evacuation process. Figure 8 represents this simulation finding.

Figures 9 and 10 present the trends in evacuation (or departure) compared to the time agents arrive at the evacuation destination. Figure 9 models the con-dition where there were no ICTs used to notify agents. The departure curve is slightly more rounded, indicating that the departure was more phased in this condition. But there is still a considerable amount of congestion, especially in the middle time period. Note that the gap between the departure and exit curves indicates high congestion.

Figure 10 shows the condition where ICTs were used to notify agents. Here we see the departure curve almost perpendicular to the axis, indicating that agents evacuated very quickly. There is also a larger gap between the departure

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Figure 7: Travel Times for Advance Evacuation Notice with and without ICTs.




and arrival time in this condition, denoting considerable congestion. The dashed line (representing the number of vehicles which successfully evacuated at a given point in time) is actually quite similar between Figures 9 and 10, suggesting that faster information dissemination does not significantly improve the time needed for travelers to evacuate. With the network and parameters in this experiment, the evacuation rate is constrained by the roadway capacity more so than the rate at which information spreads among households.

6 DiscussionThis research develops a model that can be used to further understand evacua-tion practices and impact that ICTs have on these processes. Our model found that while ICTs can expand the reach of evacuation messages and improve evacuation percentages, they also have the potential to create excessive congestion, due in

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Figure 8: Travel Times for Urgent Evacuation Scenario with and without ICTs.

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Figure 9: Cumulative Count of Evacuation Behavior in the Urgent Scenario without ICTs.




large part to the shift in the speed at which information is diffused. ICTs allow people to learn about the evacuation more quickly and contact others to verify information more rapidly; thus the evacuation-decision process is compressed. While this finding may be encouraging for emergency managers, communication personnel, and homeland security, the traffic congestion findings highlight the need to coordinate evacuation communication and transportation needs.

These findings support much of information theory and offer an example of how this theory can be applied to guide the development of an agent-based model. First, this theory predicts that since we have incomplete systems of infor-mation, we need redundancy in our messages to reach the most people. Our findings clearly show that by using multiple ICTs as part of our evacuation com-munication approach, agents had a higher sense of urgency and evacuated much more quickly than agents who only received face-to-face messages. Furthermore, by using ICTs, a higher percentage of agents evacuated, especially in the advance notice condition; a finding that also supports information theory. Our model demonstrates that ICTs change agents’s access to evacuation information, and thus, play an important role in the rate that agents evacuate.

The model can also guide evacuation-related transportation and evacuation understanding. For the network and disaster type modeled in this paper, the ulti-mate rate at which travelers evacuated the county was dictated largely by the roadway capacity. While faster information dissemination increased the rate at which travelers began evacuating, this primarily resulted in greater roadway con-gestion and time spent waiting on the road, rather than increasing the rate of successfully-completed evacuations. This suggests that emergency planners in this region, and others with similar roadway profiles, may wish to focus more on improving roadway capacity (e.g. contraflow lanes) than improving infor-mation dissemination. Conversely, in other, less-congested networks the model

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Figure 10: Cumulative Counts of Evacuation Behavior in the Urgent Evacuation Scenario When ICTs are Used.




may yield the opposite conclusion where the evacuation rate is limited more by information spread than by roadway congestion. In such cases, effective disaster management may be better aimed at helping to spread information than focusing on traffic operations. The modeling framework presented in this study allows for comparative analyses, where planners can evaluate alternative roadway capacity and information dissemination strategies to identify the most suitable practices for a particular region and disaster type.

6.1 Integrating Human Behavior and Transportation Planning

The unique contribution of this model development effort is the joint modeling of demand and supply-side variables important during evacuation practices. The major failure in the evacuation plans for Hurricane Katrina and Rita was the under-estimation of the volume of automobiles, excessive congestion, and fuel short-ages (Litman 2006). Considering the extent of ICT growth over the past decade since these events, it is critical for scholars and practitioners to understand the nuances involved in how increased communication through ICTs changes emer-gency situations. Although the prolific use of ICTs is difficult for emergency man-agers, planners, and homeland security personnel to predict, it is important for this crucial workforce to be aware of the problems that more information creates during a disaster. One of the strengths of our simulation models is that we allowed for agents’s evacuation decisions to happen over time; an approach that reflects the reality of how people make evacuation decisions (Mileti and Peek 2000). Yet even though we were cognizant of including this over-time decision process, our findings clearly highlight that increasing information and heightening urgency, produces faster evacuation decisions; an important observation as we advance our transportation infrastructure.

The analysis described above highlights the need to coordinate information-spreading and traffic management strategies: in some cases, increasing the rate at which evacuation messages were transmitted through the population did not result in travelers leaving the affected area at a substantially faster rate. While they began their trips earlier, the resulting road congestion restricted the rate at which they could exit the affected area, resulting in a similar exit curve. However, the simulation model presented here can be used to inform policy development: knowing the evacuation behavior under different information spreading assump-tions, appropriate congestion countermeasures can be adopted, such as revers-ing lanes on routes identified as subject to congestion, or re-timing traffic signals based on the predicted evacuation curves. Identifying such synergistic strategies




can lead to more rapid evacuation while mitigating congestion to the extent possible.

6.2 Limitations and Future Directions

It is important to recognize that certain intricacies of an evacuation processes did not fall within the scope of the current simulation, but could be designed into future models. First, we assumed that once agents made their evacuation decision, they did not deviate from their travel path. It is possible that agents will receive updates, through mobile technologies, radio, or roadway signs that allow them to modify and optimize their evacuation travel path. However, it is also pos-sible that most, if not all ICT systems, are inoperable during disasters that com-promise telecommunication networks (Mileti and Peek 2000). In these situations, agents will pursue an evacuation route without any updates.

Our model also does not weight socioeconomic class, gender, age, or race/ethnicity. Previous research indicates that these social factors impact evacuation decisions (Spence et al. 2007, 2011). Future models would benefit by including these factors and allow for a more tailored approach to specific community needs.

6.3 Future Research

Using data from Hurricane Ivan, Hasan and colleagues (2011) explored house-hold-level evacuation decision-making. Hasan and colleagues suggest that emer-gency managers circulate voluntary evacuation notices, along with information concerning their rationale for this recommendation, before issuing a mandatory evacuation. This recommendation complements our findings and highlights the integrated effort needed in developing effective evacuation strategies. For example, framing an evacuation as voluntary, might shift the perception that that evacuation is not as urgent, and in situations where this is ethical, it could slow the race to the road. Furthermore, using voluntary evacuations might help some agents decide to evacuate early. These early evacuators would generate a gentler evacuation wave on transportation infrastructure.

Another area for future research concerns how we educate existing resi-dents on different roads that can be used to evacuate. In particular, preparing residents for a variety of possible disasters, and overcoming habitual behavior during stressful situations, are challenges that warrant further study. Cutter et al.’s (2010) research identifying variables like access to a vehicle and evacua-tion potential could be combined with the current findings to help communities




communicate with their members. Perhaps future social marketing dollars could be used to teach our urban residents of the various routes available. Awareness campaigns like this may help show residents that their common path out of the city might not be the best choice when a disaster strikes. Cutter et al. (2010) also claim that metropolitan areas are more disaster resilient than rural communities. Since this study focused on a metropolitan area, expanding this model to study rural communities will be fruitful for additional study.

One of the more promising areas of future evacuation research uses organiza-tions to designate route options for their members. Major employers have a multi-tude of communication channels to reach their employees and stakeholders, any of which may be initiated during an emergency evacuation. If traffic engineers know that a common route will become congested during an evacuation, perhaps they can partner with major employers and have certain organizations convince their members to use an alternate route to evacuate. Obviously, this will not work for all the employees considering they can live in a large geographic area, but even if the strategy reached 5000 agents, our data suggest that it could make a considerable impact on evacuation congestion. One of the reasons this strategy might be successful is that people can “identify” or feel a part of an organization, and those feelings can transfer into desirable behavioral outcomes (Ashforth et al. 2008). For example, highly identified employees consume and share health information differently than people who feel no connection with their organiza-tion (Stephens et al. 2014a,b).

7 ConclusionAccess to ICTs has changed many contemporary practices and it is important for emergency planners to understand the links between information, human behavior, and the resulting infrastructure issues. Our study focused specifi-cally on these links and the findings reveal a more complete understanding of how evacuation messages can impact congestion. The model developed in this paper provides a framework for consistent modeling of information spreading and traffic behavior in disaster evacuations, which can be used to compare trans-portation options. These options can include information-based strategies (e.g. targeted information dissemination, or attempts to increase or decrease the rate of spread) as well as traffic management-based strategies (e.g. contraflow lanes, adjusted signal timing). As discussed above, there are many fruitful avenues for future research to build on this modeling framework to develop safer, more effi-cient disaster evacuation plans.




Acknowledgments: This material is partially based upon work supported by the National Science Foundation under Division of Civil, Mechanical and Manufac-turing Innovation, Grant No. 1254921, the National Communication Association, and the Moody College of Communication at The University of Texas at Austin. An earlier version of this manuscript was presented at the International Communica-tion Association Conference, 2015.

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Documents

Increasing Evacuation Communication Through ICTs: An Agent-based Model Demonstrating Evacuation Practices and the Resulting Traffic Congestion in the Rush to the Road