Journal of Natural Disaster Science, Volume 34, Number 1, 2012, pp41-57

Agent-based Simulation of the 2011 Great East Japan Earthquake/Tsunami Evacuation:
An Integrated Model of Tsunami Inundation and Evacuation
Erick MAS*
Anawat SUPPASRI*
Fumihiko IMAMURA*
Shunichi KOSHIMURA**International Research Institute of Disaster Science, Tohoku University, Japan(Received March 31, 2012 Accepted July 12, 2012)
ABSTRACTThe 2011 Great East Japan Earthquake/Tsunami was a magnitude 9.0 Mw event that
destroyed most structural tsunami countermeasures. However, approximately 90% of
the estimated population at risk from the tsunami survived due to rapid evacuation tohigher ground or inland. In this paper, we introduce an evacuation model integrated witha numerical simulation of a tsunami and a casualty estimation evaluation. The model wasdeveloped in Netlogo, a multi-agent programming language and modeling environment for
simulating complex phenomena. GIS data are used as spatial input information for road andshelter locations. Tsunami departure curves are used as the start time for agents deciding
to evacuate in the model. Pedestrians and car drivers decide their own goals and search
for a suitable route through algorithms that are also used in the video game and artificial
intelligence fields. Bottleneck identification, shelter demand, and casualty estimation aresome of the applications of the simulator. A case study of the model is presented for thevillage of Arahama in the Sendai plain area of Miyagi Prefecture in Japan. A stochastic
simulation with 1,000 repetitions of evacuation resulted in a mean of 82.1% (SD=3.0%) of
the population evacuated, including a total average of 498 agents evacuating to a multi-story
shelter. The results agree with the reported outcome of 90% evacuation and 520 shelteredevacuees in the event. The proposed model shows the capability of exploring individual
parameters and outcomes. The model allows observation of the behavior of individuals in
the complex process of tsunami evacuation. This tool is important for the future evaluationof evacuation feasibility and shelter demand analysis.

1. Introductiona major population evacuation during a tsunami isan accurate representation of the timing of people's
responses to the emergency (Southworth, 1991). The
timing of evacuee responses can have a significant
effect on traffic congestion and bottlenecks during

Evacuation is the most important and effective
method to save human lives during a tsunami (Shuto,2005). An important factor in realistically simulating41

E. MAS, A. SUPPASRI, F. IMAMURA, S. KOSHIMURA

the evacuation procedure (Naser & Birst, 2010). It isof course desirable that people leave the danger area
immediately after a natural or official warning is received. However, it is a fact that people sometimes areapparently irrational and do not evacuate even when
the authorities suggest they should (Riad, Norris &Riback, 1999). Imamura (2009) suggests three steps
that can lead to a safe evacuation during an earthquake and tsunami event: first, collecting informationand issuing an official warning; second, making thedecision to evacuate based on the risk perception andprevious experience of the area residents; and third,
selecting a proper route and a safe destination for theevacuees. Prior experience and the perception of risk
will differ between people. In this study, a tsunami
evacuation simulator was developed to explore theindividual behavior of start time of evacuation, as
well as to make casualty estimation and elucidate issues related to the process of evacuation in the case oftsunami. Tsunami modeling results are integrated into
the model to estimate casualty conditions based on thehydrodynamic characteristics of tsunami. The agentbased approach allows for tracking the individual
evacuation decision, route, and shelter.

of approximately 30 m (USGS, 2011). The maximum
recorded earthquake intensity was 7, which is the
maximum value on the Japanese intensity scale (JMA,2011). The earthquake early warning system was activated 8 sec after detection of the first P-wave (JMA,2011), while a tsunami warning was issued 3 minafter the earthquake. The initial tsunami warning forMiyagi Prefecture, estimated a 6 m tsunami height;
after 24 min, a second warning bulletin was issued
increasing the tsunami height estimate to over 10 m
(JMA, 2011). The tsunami reached the coastal villageof Arahama approximately 1 hr after the earthquake.
All told, the tsunami casualties numbered 19,212 deadand missing and more than 50,000 damaged houses
and buildings (National Police Agency (NPA), 2012;
Suppasri, Koshimura et al., 2012a).
2.2 Tsunami Evacuation
The Japan Meteorological Agency, the Fire andDisaster Management Agency, and the Cabinet Officeof Japan conducted a joint survey of 870 evacuees atshelters in Iwate (391), Miyagi (385), and Fukushima(94) in July 2011 (Cabinet Office of Japan, 2011). The
results showed that 57% of the interviewees evacuated immediately after the earthquake while 37% had
delayed their evacuation. The two main reasons given
for evacuation were the strong ground motion (48%of evacuees) and the advice to evacuate given by family members (20% of evacuees) or neighbors (15% ofevacuees). Another survey conducted in Kamaishi andNatori Cities found that 60% of 113 interviewees in
Kamaishi evacuated in less than 10 min, while in Natori, just 30% of 105 respondents escaped within 20
to 30 min. Based on tsunami waveform data recorded
during the event (Hayashi, Tsushima, Hirata, Kimura& Maeda, 2011) and clocks observed in the field confirming the arrival time of waves (Muhari, Imamura,Suppasri & Mas, 2012; Suppasri, Mas et al., 2012b),
the estimated tsunami arrival times in Kamaishi andNatori were approximately 30 min and 67 min, respectively (Yalciner et al., 2011). It was observed thata large number of evacuees from the coast left by car,especially from low-topography areas like the Sendai
plain.

2. The 2011 Great Japan Earthquake and Tsunami
2.1 Overall damage
Japan has experienced many earthquakes andtsunamis throughout its history, particularly along
the Sanriku coast. Japan has therefore developed theworld’s best tsunami countermeasures and evacuation procedures. Before the 2011 event, the area ofmost concern to seismologists was Miyagi Prefecture, where the calculated probability of the expected
Miyagi-oki earthquake occurring within 30 years
with a magnitude of 7.5-8.0 Mw was estimated to be
99%, which was the highest earthquake probability
in Japan. At 14:46 on March 11, 2011, a massive
earthquake occurred offshore at N38.1, E142.9 at a
depth of 24 km (JMA, 2011). The earthquake had amagnitude of 9.0 Mw and was followed by many aftershocks and a devastating tsunami. The earthquake
was ranked as the fourth most powerful in the world,
exceeded only by the 1960 Chile (9.5 Mw), 2004
Sumatra (9.3 Mw), and 1964 Alaska (9.2 Mw) earthquakes. The earthquake was of long duration (approximately 3 min) and the largest observed slip was
42

AGENT-BASED SIMULATION OF THE 2011 GREAT EAST JAPAN EARTHQUAKE / TSUNAMI EVACUATION

3. Tsunami Modelingnumerically simulate the tsunami entering the villageof Arahama in the Sendai plain in Miyagi Prefecture.
Tohoku University’s Numerical Analysis Model for
Investigation of Near-field Tsunamis (TUNAMI model) was used as the modeling tool (Imamura, 1995).
A set of non-linear shallow water equations, (Eq. 1 toEq. 3), were discretized by the staggered leap-frog finite difference scheme, with bottom friction expressedusing Manning’s formula and set to be constant acrossthe whole domain.

3.1 Tsunami source model
The tsunami source was modeled assuming
an instantaneous displacement of the sea surface
identical to the vertical sea floor displacement. Ten
sub-fault segments based on the Tohoku University
Source Model version 1.1 (Imamura et al., 2011) wereused for the initial surface deformation (Fig. 1). The
parameters are shown in Table 1.
3.2 Numerical model setup
The source model presented above was used to

డఎ
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Figure 1 Tohoku University Tsunami Source Model version 1.1 of the 2011 Great East Japan Earthquake. The
five regions show the nested grid setting for the numerical simulation.

Table 1 Dimension of the faults and tsunami source parameters for the 2011 Tohoku Earthquake

43

(1)

E. MAS, A. SUPPASRI, F. IMAMURA, S. KOSHIMURA

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“agent”-based approach. This means that the model
assumes that there are thousands of individual agents
who each have their own characteristics that follow
simple rules in moving toward a few different goals.

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(2)

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4.1 Overview of the model
The model for tsunami evacuation was developed in Netlogo, a multi-agent programming language
and modeling environment for simulating complex
phenomena (Wilensky, 2001). Hundreds or thousandsof agents can operate concurrently to explore the connection between the micro-level behavior of individuals and the macro-level patterns that emerge fromtheir interactions. The model uses GIS data as spatial
input and, if available, the population spatial distribution. Tsunami characteristics are introduced as raster
files at each tsunami numerical time step during thesimulation. Input parameters of scenario, model view,
and outputs are observed during the simulation in realtime. Outputs are exported into file reports. A snapshot of the model interface is shown in Figure 2.

ேమ

ቀ ቁൌ
஽

ܰξ‫ܯ‬ଶ ൅ ܰ ଶ Ǥ

(3)

Here:
ఎ

ఎ

‫ ܯ‬ൌ ‫ି׬‬௛ ‫ ݖ݀ݑ‬Ǣ ܰ ൌ ‫ି׬‬௛ ‫ ݖ݀ݒ‬Ǣ ‫ ܦ‬ൌ ߟ ൅ ݄.
M and N are the discharge flux in the x and y directions, respectively, η is the water level, and h is thewater depth above the mean sea level.
Bathymetric and topographic data were obtained
from the Central Public Disaster Prevention Councilof Japan. Grid sizes vary from 405 m, 135 m, 45 m,
15 m, and 5 m in five regions of a nested grid system
as shown in Fig. 1.

4.2 Start time of evacuation
To realistically simulate a tsunami evacuation
event, it is important to accurately represent the timing of responses to the emergency (Southworth, 1991;
Suzuki & Imamura, 2005). The timing of evacuee
responses can have a significant effect on traffic congestion and bottlenecks during an evacuation (Naser& Birst, 2010). Moreover, it is expected that humandamage will decrease if there are more early evacuations (Sugimoto, Murakami, Kozuki & Nishikawa,
2003). Past simulation efforts on the evacuation ofhumans from tsunamis have considered timing using
many approaches that have resulted in differing estimates of casualties and congestion points. Three main
approaches to start time have been used in prior tsunami evacuation modeling work. The first approach isan “all together” evacuation based on scenarios where
the entire study population starts evacuating a certain number of minutes, such as 0 min or 5 min, after
the warning (Suzuki & Imamura, 2005; Sugimoto,
Murakami, Kozuki & Nishikawa, 2003; Fujioka, Ishibashi, Kaji & Tsukagoshi, 2002; Ohhata, Kagami &Takai, 2005; Post et al., 2009; Meguro & Oda, 2005).
However, such instantaneous group behavior has never been recorded in past events. In general, individuals from a large at-risk population never start their

4. Evacuation Modeling
Tsunami hydrodynamic models have significantly contributed to the scientific and engineering
investigation of tsunamis. However, understandingsocial science and risk management is necessary for
a complete modeling of the problem. Evacuation isprobably the most important and effective method tosave human lives (Shuto, 2005) and more emphasis
has recently been put on social science in understanding tsunami mitigation. Thus, modeling tsunamis ispart of modeling evacuation from a tsunami. However, human behavior is very complex and is the mostdifficult aspect of the evacuation process to simulate(Gwynne, Galea, Owen, Lawrence & Filippiddis,1999). Human behavior includes complex problem
solving (Gagne, Wager, Golas & Keller, 2004), andindividual characteristics with both aspects are difficult to capture in mathematical equations (Pan, Han,
Dauber & Law, 2007). In this study, we developed
a tsunami evacuation model considering the outputs
described above. The model of evacuation uses an
44

AGENT-BASED SIMULATION OF THE 2011 GREAT EAST JAPAN EARTHQUAKE / TSUNAMI EVACUATION

Figure 2 Snapshot of the interface of the developed tsunami evacuation simulator. Parameter input panel (left),view screen (center), and output graphs (right) can be observed together during simulation.
evacuations at the same time due to individual decisions. The second approach is similar to the previous
one, but fixed start times are assigned to groups or areas based on survey results (Imamura, Suzuki & Taniguchi, 2001; Saito & Kagami, 2005). This is a special
case of the first approach where the whole population
is divided into smaller groups and the average startingtime of the group is obtained from questionnaires distributed in the area. Although this may lead to better
approximations for the evacuation process of a large
population, the use of one questionnaire result may
not explain the complexity and uncertainty of humanbehavior or the initial spatial condition of the population. The third approach is a more sophisticatedapproach that introduces psychological parameters
obtained from questionnaires and treats people individually with respect to displaying rational behavior(Sato, Kono, Koshimura, Yamaura & Imamura, 2008;
Mas, Imamura & Koshimura, 2011). For this third
approach, however, the definitions of parameters andvalues for the simulation are difficult to assess for alarge population. In this paper, we will introduce afourth approach modified from the emergency, urban,
and traffic planning fields in which departure times
are determined from sigmoid curves describing the
population (Lindell & Prater, 2007).

common to researchers and stakeholders, but conducting surveys and updating survey data usually faces
budget and time constraints, leading to delayed decisions for new mitigation actions. According to Naser& Birst (2010), surveys that are designed to collectdata describing actual travel or evacuee behavior
are classified as Revealed Preference (RP) surveys,
while hypothetical behavior in the future is obtained
through Stated Preference (SP) surveys. Hence, RP
surveys are related to past tsunami experiences, while
SP surveys collect data on how the evacuee would
respond to a hypothetical situation in the future. Bothsurvey types attempt to obtain estimates of the human behavior during an emergency; however, theuse of one questionnaire result may not explain thecomplexity and uncertainty of human behavior or the
initial spatial condition of the population. Moreover,there is a frequent variation in the level of awarenessand risk perception of individuals resulting from recent experience, public information, or education. As
a result, previous estimations of evacuation times inpast surveys may change in time. Changes could also
arise because events have now been forgotten or there
might now be more estimates (Tatano, 1999).
4.4 Tsunami evacuation departure times
To address the complexities and variabilities ofhuman behavior and preferences regarding evacuation
times, a set of distributions of departure times can beused to include all possible behaviors in the population. Such an approach requires a definition of suitable distribution parameters. Researchers developing

4.3 Questionnaire surveys
Tsunami evacuation models often use data provided by questionnaire surveys to establish average
evacuation start times or an estimated distribution ofevacuation decision times. The need for these data is45

E. MAS, A. SUPPASRI, F. IMAMURA, S. KOSHIMURA

state-of-the-art techniques for modeling evacuation
departure times in large-scale events such as hurricanes or nuclear accidents have used sigmoid curves
to describe the population load rate into the evacuation network (Lindell & Prater, 2007; Southworth,
1991; Tweedie, Rowland, Walsh, Rhoten & Hagle,1986). In this work, we used several RP and SP surveys of tsunami evacuation and compared them withthe theoretical Rayleigh distribution (Eq. 4), which issimilar to the shape proposed by Tweedie, Rowland,
Walsh, Rhoten & Hagle (1986) for regional evacuation in a hurricane.

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,

rameter or mode of distribution can be related to the
distribution mean μ (Eq. 5).
Table 2 shows the results of correlations and values of the distribution mean compared to the reported
or estimated tsunami arrival times in RP or SP surveys, respectively.
As can be observed in Fig. 3a and Fig. 3b, there
is a higher correlation between the recorded arrival
times of tsunamis with the preparation times from RP
surveys compared to the estimated arrival time andthe preparation times stated in SP surveys.
This means that an SP survey might be obtaining
from respondents what it is considered to be the “correct” answer, which is a fast evacuation. An RP survey may show instead that at least half of the population at risk waited until the tsunami had nearly arrived
before they started their evacuation. This apparently
irrational behavior of some people in delaying theirevacuation activity has been observed in other eventsand confirmed by several videos available on the web
for this latest tsunami event. Based on Fig. 3, one
might be tempted to rely on the distributions of previous revealed preference surveys for input into theevacuation model. However, due to individuality of

(4)

(5)

Here:
F(t) is the Rayleigh cumulative distribution of
the decision rate for evacuation of the population
at time t. The parameter σ is the continuous scaleparameter of the Rayleigh distribution. This scale pa-

Table 2 RP and SP questionnaire surveys correlated to Rayleigh distributions

(a)

(b)

Figure 3 (a) Stated Preference surveys; (b) Revealed Preference surveys.
46

AGENT-BASED SIMULATION OF THE 2011 GREAT EAST JAPAN EARTHQUAKE / TSUNAMI EVACUATION

behavior, we cannot neglect the stated preference surveys because some individuals might actually follow
the stated preferences. A better approach, therefore, is
to assume a behavior bounded by the SP and the RP
results.
It is the case that many areas desiring a tsunami
risk assessment might not have had a tsunami recently, meaning that RP surveys might not be available. In
this case, based on the results explained before, stakeholders may apply SP surveys and estimate the arrival
time of a tsunami through numerical simulations. The
final boundary distributions of behavior are obtained
from these two methods.

move around the streets in the model. This means thatcollision avoidance should be included in the model.
Agents move in a continuous grid space according
to their actual speed. An agent does not necessarily“jump” from the center of a grid square to anothercenter (integer coordinates), but may step on borders
or other parts of a grid square between the centerand the borders (real coordinates). To allow agents tomove, a certain number of agents are allowed in anarea based on the spatial accuracy or grid size. If apedestrian requires a 1 m2 personal area, 25 pedestrians can theoretically “fit” in a 5 m x 5 m grid. However, due to the dynamic movement of agents and theconsideration of personal space between individuals,
the grid area is not filled as would be suggested bytheory. We used the predictive collision avoidance
method proposed by Karamouzas, Heil, van Beek &Overmars (2009) and tested the movement of pedestrians from the area via a corridor (Helbing, 1991).
We count the number of agents in a 5 m x 5 m area
moving along a 1 m x 1 m grid-size corridor in a time
step; the results show that the maximum capacity of
the grid is 70% of its total area (Fig. 4). Therefore, weestablished a congestion condition of no more than
70% of the grid space used for pedestrians. A similar
approach determined that the congestion condition
for cars was 7%. Thus, a maximum of 18 pedestrians
are allowed in a 5 m x 5 m grid, which is the limit for
defining a crowd condition. This means that agents
who would be modeled as wanting to enter a crowded
space must wait until another agent has left the spaceand made room. The crowded condition for cars allows a maximum of two cars on a 5 m x 5 m grid.

4.5 Agent architecture
We model individuals as agents having theminimum necessary ability to process informationand execute their evacuation by simple behaviors or
decisions broken into layers. The layers are described
below.
a) Layer 0: The evacuation decision, with times
assigned randomly based on the departuretime distributions discussed aboveb) Layer 1: The shelter decision has two alternatives available in the scenario: One alternative
is to go to the closest shelter, while the otheralternative is to choose any shelter. Other
models have traditionally used the nearest
shelter condition, but in many cases, the preference is not necessarily the nearest shelter.
c) Layer 2: Pathfinding: As a method of finding aroute that was not necessarily the best or closest, we used the A* (A star) algorithm on grid
spaces. This is the most popular graph searchalgorithm and is also used in the video gameindustry (Anguelov, 2011).
d) Layer 3: Speed adjustment: Speed variability
is assumed to be a one-tailed normal distribution with density based on the field of viewfor the agent and a maximum value of 1.33
m/s for pedestrians and 30 km/h (8.33 m/s)
for cars (Meister, 2007; Suzuki & Imamura,2005). The field of view for the agent is a 60°
cone with a 5 m radius for pedestrians and a
10 m radius for vehicles.

4.7 Casualty estimation
We modeled casualties using the experimental
results of Takahashi, Endoh & Muro (1992). The
results provided a binomial casualty condition (safeor fall) depending on the flow depth (cm) and flow
velocity (cm/s). The results of this experiment wereused as a reference for the characterization of humanstability against hydrodynamic forces; it reflects theinteraction of the human body with flow features suchas the depth and velocity that the tsunami may alsopresent. A binomial logistic regression was performed
to obtain the casualty probability as a function of flow
characteristics. The results are shown in Fig. 5 andEq. 6.

4.6 Agent movement
A large number of both pedestrian and car agents47

E. MAS, A. SUPPASRI, F. IMAMURA, S. KOSHIMURA

Figure 4 The corridor test (Helbing, 1991) using the predictive collision avoidance method (Karamouzas et al.,2009) to find the maximum capacity of agents in a certain area as a congestion parameter for bottleneckcalculation. In the dynamic condition of agents, only 70% of the total area is used for movement withcollision avoidance.

Figure 5 Binomial logistic regression model from experimental data in Takahashi et al. (1992).

48

AGENT-BASED SIMULATION OF THE 2011 GREAT EAST JAPAN EARTHQUAKE / TSUNAMI EVACUATION݂ ሺ‫ ݖ‬ሻ ൌ

2,704 residents lived in this area; after the 2011 tsunami, the Sendai City Bureau reported a total of 2,421
residents (Census, 2011). The population reductionof 283 might be the maximum number of casualties
because after the earthquake, the local media reported
that between 200 and 300 victims had been found in
the area. The tsunami arrived in Arahama 1 hr after
the earthquake with a maximum wave height of 10 m.
The tsunami inundated 5 km inland, approximately
ten times the expected Miyagi-oki tsunami. Arahama
does not have many highly reinforced concrete buildings, so the only official tsunami evacuation buildingin the area is Arahama Elementary School, which has
four stories and an accessible roof. It remained standing after the earthquake and provided tsunami shelter
for approximately 520 evacuees (NGA, 2011). Figure
6 shows the synthetic waveform produced by numerical simulation of the tsunami 350 m offshore and at a
7.5 m depth in front of Arahama. From the numericalsimulation, the estimated arrival time of the first peakof the tsunami wave was 69.5 min; this estimate wasconfirmed by video footage and agreed with stopped
clocks found in the area (Suppasri, Koshimura et al.,2012a). For instance, a clock found inside the onestory gymnasium at Arahama Elementary School had
stopped at 15:56, 70 min after the earthquake (Fig.
7a), while a second clock found in the surrounding
residences had also stopped at 15:56 (Fig. 7b). In an
elevated children’s playground in Arahama Adventure
Park, 2 km south of the Elementary School, a third
clock found inside the information office had stopped
at 15:58 (Fig. 7c). At this location, the tsunami heightwas 2 m while in other places in the Arahama area,
the tsunami height reached 7 to 8 m. The office wasapparently protected by the hill that split the tsunami
wave in two directions. Finally, a fourth example wasat Higashi-Rokugo Elementary School, located 2 km
further inland. In this location, a clock had stopped at
16:03, 77 min after the earthquake (Fig. 7d).

ଵଵା௘ሺభఱǤరఴష೥ሻ .

(6)

Here:
z=β0 + β1 * h + β2 * u.
β0 = -12.37; β1 = 22.036; β2 = 11.517.
h = tsunami inundation depth.
u = tsunami velocity.
The experimental data are limited to depth andvelocity intervals of h [0.28, 0.85](m) and u [0.50,
2.00](m/s). Values for tsunami features above theseranges cannot be probabilistically calculated with thedeveloped binomial function. Therefore, the model
applies the binomial function to the estimation of casualties when the tsunami conditions are within range,
and when the depth is greater than 0.85 m, the trapped
agent is considered to be a casualty.
Casualties were also estimated for cars andagents in cars. While there have been cases where
evacuees were found safe inside a car after a tsunami,in the majority of cases, passengers caught by a tsunami do not survive because they drown in the car or
succumb due to debris impact. Based on Yasuda &Hiraishi (2004), a value of 0.50 m of inundation depth
is considered sufficient for a driver lose control ofthe vehicle and in many cases, the car begins to float.
Thus, when a car is trapped in a tsunami inundation
depth greater than 0.50 m, it is considered to be a casualty with all of its passengers (four by default).
4.8 Outputs in model
Model outputs are presented in three ways: a)model view; b) reporters; c) files. The model viewshows the movement of agents and tsunami propagation. Reporters show the simulation results of thenumber of saved agents or casualties, plots of shelter
demand, and the evacuation rate. Files report the total
simulation step by step in text, image, and video format.

5.1 Spatial and tsunami data
Local spatial data for the simulation were provided by the Geospatial Information Authority ofJapan (GSI) in a shape format and converted for themodel into a 5 m x 5 m grid raster. Tsunami data weretaken from the output of calculations in the smallest
domain shown previously (Fig. 1 & Fig. 2).

5. Case Study: Arahama Village
Arahama is a populated village in Wakabayashi
Ward of Sendai City in Miyagi Prefecture, Japan. Arahama is located between the Natori and the Nanakita
Rivers, 6 km south of the Port of Sendai. A total of49

E. MAS, A. SUPPASRI, F. IMAMURA, S. KOSHIMURA

Figure 6 Synthetic waveform of a tsunami 350 m offshore of Arahama village (7.5 m sea depth).
Arrival time: 69.5 min; height: 9.69 m.

Figure 7 Possible evidence of the tsunami arrival time from stopped clocks. The time agrees with video footage,
witnesses’ accounts, and numerical simulation of tsunami arrival time. a) Arahama Elementary School
gymnasium (15:56); b) Arahama village (15:56); c) Adventure Park (15:58); d) Higashi-Rokugo
Elementary School (16:03).

50

AGENT-BASED SIMULATION OF THE 2011 GREAT EAST JAPAN EARTHQUAKE / TSUNAMI EVACUATION

inundation. Witness accounts and rescue teams statedthat the majority of Arahama evacuees were found in
Arahama Elementary School, which was a designatedevacuation building. The rest of the population was
evacuated by car along two main roads, as shown in
Fig. 9. The first evacuation road (Exit 1) was the Prefectural Road No. 137, while the second road (Exit2) was an alternative road towards the inland area.
Both pedestrians and cars in our model were allowed
to evacuate to the evacuation building in the area, but
only cars were allowed to evacuate via the nearest
road to inland areas.

5.2 Population data
It is very difficult to determine the exact numberof evacuees at the moment of an earthquake. However, an estimation of a possible number of agentsin the area is considered. It is not within the scope inthis paper to fully reproduce the evacuation of March
11th, but to introduce the model’s capabilities with theavailable data for a real event. The model was applied
to 84% of the Arahama residential area, so 84% of
the population of 2,704 (2,271) was considered. Of
this group of evacuees, 72% were taken to be in-car
evacuees based on the questionnaire results by Suzuki& Imamura (2005) (Fig 8). Assuming there were four
passengers per car, 410 cars were modeled plus 631
pedestrians.

5.4 Stochastic simulation
The use of a multi-agent paradigm or agentbased model typically requires stochastic elements(Ormerod & Rosewell, 2009). Consequently, a setof runs was conducted to obtain average outputs forparameters of interest. We conducted several trials toobserve the convergence of estimated values over alarge number of repetitions. Therefore, 1,000 simulation repetitions were conducted with random initial
spatial distributions of pedestrians and cars. The evacuation start time decision was based on a randomly
selected value in a distribution bound between resultsfrom Suzuki et al. (2005) and the recorded arrival
time of the tsunami on March 11th (67 min), with a
distribution mean of 7 min (Fig. 10).

5.3 Hazard map, shelters, and routes
Before the 2011 event, Miyagi Prefecture andSendai City were expected to be subjected to a 7.5 to
8.0 Mw magnitude earthquake within 30 years with a
99% probability of occurrence. In this projection, thepredicted damage area was the northern part of Tohoku, the Sanriku coast; while the Sendai plain might
be inundated less than 1 km inland with a nearly 3 m
tsunami height. The hazard map prepared for this projection is shown in Fig. 9. The delimited area included
no more than 500 m of shoreline. The large magnitude of the Great East Japan Earthquake produced atsunami with an unprecedented height that reachedapproximately 5 km inland, ten times the predicted

Figure 8 Questionnaire results of evacuation mode preferences, from Suzuki et al. (2005).
(TEB: tsunami evacuation building)51

E. MAS, A. SUPPASRI, F. IMAMURA, S. KOSHIMURA

Figure 9 Tsunami hazard map before the 2011 tsunami. Actual inundation includes the total area shown in thegraph. The inset square shows the area of simulation. Also, Exit 1, Exit 2, and the tsunami evacuationbuilding (TEB) of Arahama Elementary School are annotated. (Sendai City website: http://www.city.
sendai.jp/s-map/bousai.html)

Figure 10 Boundary distributions for the start time decision of evacuation from a tsunami. Fast evacuation isdetermined by the questionnaire answers, while slow evacuation is based on the arrival time of thetsunami calculated by numerical simulation. Inside the area bound by the two extreme expectedbehaviors is a distribution of evacuation decisions from the 1,000 repetitions of stochastic simulation.

5.5 Results and discussion
Each simulation provides information comprising the number of evacuees sheltered plus the numberof evacuees who have passed one of the exits (“safe”),
the number of evacuees trapped by the tsunami withmore than a 50% probability of falling (“casualty”),
and the number of evacuees in tsunami evacuationbuildings (TEBs). The average, standard deviation(SD) and other statistical values for the set of repetitions are shown in Table 3. The results show that52

AGENT-BASED SIMULATION OF THE 2011 GREAT EAST JAPAN EARTHQUAKE / TSUNAMI EVACUATION

82.1% (SD=3.0%) of the population was safely evacuated. The apparent preferences for evacuation shelters are balanced between the three given options. An
average of 21.9% (SD=0.5%) decided to evacuate to
the tsunami evacuation building (TEB), while nearly
30.1% (SD=2.9%) and 30.0% (SD=2.8%) preferred
to exit the area through Exit 1 and Exit 2, respectively. According to local media and the city census
results before and after the earthquake, approximately
90% of the population was saved from the tsunami,
as approximately 283 casualties were found in thearea and 520 evacuees were sheltered in the Arahama
Elementary School building. It is difficult to obtain
the exact values observed with the stochastic simulation developed in this study; however, the average
estimations of survivors and evacuees at the TEB
show that the model has good capability of reproducing a suitable representation of decisions and casualty
estimations in the area. The relative error of predictedvalues and the official information for the real event
was 43% for the casualty estimation (model: 406,
data: 283), 4% for the number of evacuees shelteredat the tsunami evacuation building (model: 498, data:520), and 7% for the rest of the population evacuating
inland (model: 1,367, data obtained as the difference
between casualties and those sheltering with the 2,271
modeled: 1,468). Due to the uncertainty in the initial
population distribution and the possible use of nonofficial buildings as shelters, the casualty estimation
cannot be accurately predicted despite the improved
results shown in Mas, Imamura & Koshimura (2012)
compared to other methods of simulation. Figure 10
shows one of the 1,000 simulations of randomly selected values for the evacuation decisions of all agentswithin the given boundaries. The average distribution
follows a sigmoid shape. By the time the tsunami arrived (t=67 min), a total of 85% of the population had
already decided to evacuate. An important characteristic of micro simulations is the capability to explore
the behavior of the model variables at the individuallevel. For example, the simulation allows a focus on
the departure times shown in Fig. 10 for the entire
population involved in the scenario, taking into consideration the casualty outputs in the model. A graphof one simulation showing sheltered evacuees’ departure times (“survivors”) next to the evacuation start
times for agents caught by the tsunami (“casualties”)
is shown in Fig. 11. The influence of late departure

times on the casualty estimation is clear. The majorityof casualties decided to start their evacuation after approximately 53 min with some even evacuating after
70 min. By contrast, most of the survivors decided toevacuate after much less time. Some agents who started evacuating after 53 min made it to their shelter due
to their specific distance to the shelter or having rapid
modes of evacuation. We divided the modes of evacuation for the sheltered evacuees into pedestrians andcars and show in Table 4 the average distance to theshelter, the start time of evacuation, the time of evacuation considering only their travel time to the shelter,
the total evacuation time, and the average speed. The
size of the model area means that the average distanceto the shelter is approximately 500 m. Because wehave used the random seed number and the same algorithm to decide departure times for pedestrians andcars, the start time of evacuation values are similar.
However, as expected, the time of evacuation (ToE)is shorter for cars than for pedestrians in accordancewith the given speed rules. The total evacuation timeis almost the same because of the great influence ofthe departure time. The average pedestrian speed of
1.10 m/s agrees with the maximum value of 1.33 m/
s developed above. The average car speed of 5.03 m/
s (18.11 km/h) was also consistent with the maximum
value given by the model rule. The average speed
values are lower than the maximum model rules, due
to the dynamic condition of increasing and decreasing
speed from bottlenecks and crowd conditions.
It is important to go beyond the final simulation
outcomes for sheltered evacuees or residents evacuating through exit points and to evaluate the demand on
resources during the evacuation process. A step-bystep tracking of the demand for shelters over time andthe traffic flow through exits is possible and usefulfor resource management for supporting evacuation.
Due to the stochastic characteristic of this simulation,
it is difficult to show the set of 1,000 outputs. Figure
12 shows one of the repetition outputs. In this case,
30 min after the earthquake, only 37.2% of the population has reached their goal, 8.3% to the TEB, and13.6% and 15.3% to Exit 1 and Exit 2, respectively.
Bottleneck conditions in every repetition are shown ina final snapshot of each simulation (Fig. 13). Traffic
was generally observed on the main roads leading to
the exit points, at the entrance of the tsunami evacu53

E. MAS, A. SUPPASRI, F. IMAMURA, S. KOSHIMURA

Figure 11 Departure times of agents and their final condition as survivor or casualty. The influence of fasterevacuation is clear in the graph.
ation building, and near the bridge at the channel.
Although these bottlenecks were not all present all at
the same time in a given simulation, bottlenecks wereobserved at some point in at least one of the repetitions. All of these bottlenecks should be identified as
critical points. Future work will involve exploring anadequate representation of the entire set of outcomesto facilitate the understanding of the evacuation process.

6. Conclusions
An integrated tsunami and evacuation simulator
was introduced. The TUNAMI model and the Tohoku
University Source Model version 1.1 was the tsunami
modeling tool applied to the evacuation scenario forthe 2011 Great Tohoku Earthquake and Tsunami. The
evacuation model of evacuation was developed in
NetLogo considering the influence of human behavior
and individual characteristics on when an individual
starts evacuation. The analysis of several questionnaire surveys with a statistical distribution showed

Figure 12 The curves show the loading of evacuees into the shelter (TEB) and through exits (Exit 1 and Exit 2)during the time of evacuation. After 30 min of simulation, less than 40% of population had found their
way to the shelter or out of the computation area.
54

AGENT-BASED SIMULATION OF THE 2011 GREAT EAST JAPAN EARTHQUAKE / TSUNAMI EVACUATION

Figure 13 Representative snapshots of two different repetitions of simulation. Bottlenecks can be observed in thecircled areas. Near the exit points, the evacuation building and a bridge connecting two areas are separated by a channel.
that stated preferences surveys indicated an expected
fast evacuation by respondents, while revealed preference surveys showed a later distribution of evacuation
start times. In this study, we have used human behavior data from questionnaires of stated preferences andfeatures of tsunamis obtained by numerical simulation, such as the arrival time, to construct boundary
distributions for a stochastic simulation of evacuation
decisions. Tsunami hydrodynamic conditions wereused for the casualty estimation. A case study was
conducted through simulation of the Arahama evacuation in the 2011 Great Tohoku Earthquake and Tsunami. The case study shows the capability of the modelof exploring individual parameters and outcomes.
Evacuation models like the one introduced here allows for studying the behavior of individuals in thecomplex process of tsunami evacuation.

tional Research Institute of Disaster Science (IRIDeS)at Tohoku University.
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