Validation of Evacuation Decision Model:

An Attempt to Reproduce Human Evacuation Behaviors

during the Great East Japan Earthquake

Akira Tsurushima

SECOM CO., LTD., Intelligent Systems Laboratory, Tokyo, Japan

Keywords:

Evacuation Behavior, Herd Behavior, Decision Making, Video Analysis, Response Threshold Model.

Abstract:

The evacuation decision model was developed to represent human herd behavior during disaster evacuations

and employed to analyze symmetry breaking phenomena in evacuation exit choices. However, it has yet to be

tested on actual human evacuation data. By examining video footage recorded during the Great East Japan

Earthquake, we discovered unusual evacuation behaviors previously unreported in the literature. Those being

the choice between ﬂeeing and the drop-cover-hold on action. These behaviors formed a unique spatial pattern

when observed in a room. In this study, we attempt to reproduce this unique human evacuation behavior via

multiagent simulations using the evacuation decision model and demonstrate that simple herd behavior is

sufﬁcient to reproduce the spatial pattern of the evacuation decisions.

1 INTRODUCTION

In constructive approaches, model validation is cru-

cial but in this case difﬁcult, due to the limited amount

of available objective data since our model domain is

human behavior during disasters. The evacuation de-

cision model (Tsurushima, 2019a) was developed to

represent human herd behavior during disaster evac-

uations. It is employed to analyze symmetry break-

ing phenomena in evacuation exit choices which are

typical in disaster evacuation situations (Tsurushima,

2019b; Tsurushima, 2019c). Herd behavior refers

to the mentality of individuals that make decisions

based on other people’s choices or behaviors instead

of their own intentions or analysis. Herd behavior

is a cognitive bias, a mental tendency yielding erro-

neous behaviors or irrational decisions resulting in

unfavorable outcomes, which can often be observed

in disaster evacuations (Cutter and Barnes, 1982; El-

liott and Smith, 1993). Symmetry breaking in exit

choice is a phenomenon observed when people evac-

uate from a room with two identical exits, in which

the exits are often unequally used and evacuees gather

at one of them. These behaviors result in the inefﬁ-

cient use of exits, increasing the total evacuation time.

The evacuation decision model has successfully re-

produced symmetry breaking phenomena in evacua-

tion exit choices; however, it is yet to be tested using

real human evacuation data owing to the difﬁculty of

obtaining those data.

Research on human behaviors during disaster

evacuation has primarily been conducted through in-

terviews (Mas et al., 2012; Drury et al., 2015), labora-

tory experiments using humans (Schmidt and Galea,

2013; Garcimart

ın et al., 2014), or laboratory experi-

ments using non human animals (Saloma et al., 2003;

Altshuler et al., 2005; Ji et al., 2017). However these

techniques are limited because of the following:

1. interviews can only be conducted with survivors,

2. it is difﬁcult to reproduce the mental pressure of

real evacuations in laboratory experiments, and

3. animal behaviors are not necessarily identical to

human behaviors.

None of the above assures that data obtained by those

methods refer to real human behaviors in disaster sit-

uations.

With the increase in surveillance cameras and

smartphones, video images of human behavior during

disasters have been recorded; recently, those videos

have been analyzed and human evacuation behav-

iors investigated (Yang et al., 2011; D’Orazio et al.,

2014b; Gu et al., 2016; Hori, 2018). Problems such

as accessibility and poor video quality have limited

the success of these approaches (Shiwakoti and Sarvi,

2013).

Tsurushima, A.

Validation of Evacuation Decision Model: An Attempt to Reproduce Human Evacuation Behaviors during the Great East Japan Earthquake.

DOI: 10.5220/0008874300170027

In Proceedings of the 12th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2020) - Volume 1, pages 17-27

ISBN: 978-989-758-395-7; ISSN: 2184-433X

A video clip captured in a meeting room in Sendai

during the Great East Japan Earthquake of March 11,

2011 (Fig. 1) is exceptionally valuable for the follow-

ing reasons:

• the earthquake was captured in one continuous

scene from the beginning to the end,

• the initial position of the people in the room when

the shaking began was clearly recorded, and

• the professionalism of the camera crew rendered

it relatively easy to examine the behavior of each

individual during the earthquake.

By studying this video, real human behaviors during

an earthquake can be analyzed. In this study, by re-

producing human evacuation behaviors observed in

the video, we attempt to conﬁrm the validity of the

evacuation decision model.

The aim of this paper is twofold. First, by an-

alyzing the behavior of the individuals in the video,

unusual human behaviors that are yet to be reported

in the literature are introduced and examined. Sec-

ond, for validation, unusual human behaviors are re-

produced by multiagent simulations using the evacu-

ation decision model.

2 RELATED WORK

Because real-life experiments of earthquake evacua-

tions are difﬁcult to perform owing to the complex-

ities of such environments, studies on human evac-

uation behaviors during earthquakes have been con-

ducted through surveys and interviews.

Kimura et al. conducted a study to understand

the behavioral and psychological reconstruction pro-

cesses of victims in the 2004 Mid-Niigata prefec-

ture earthquake through a survey of sample size 543

(Kimura et al., 2006). Mas et al. estimated dis-

tributions of departure times of tsunami evacuations

through revealed preference and stated preference

surveys (Mas et al., 2012). Drury et al. studied the

solidarity behaviors of evacuees of the 2010 Chile

earthquake by interviewing 1240 people (Drury et al.,

2015). Morita et al. interviewed people in Yamada-

machi and Ishinomaki-shi to investigate the pre-/post-

earthquake evacuation behaviors of the Great East

Japan Earthquake (Morita et al., 2015).

Recently, novel approaches using video to analyze

human behaviors during earthquake evacuations have

emerged.

Yang et al. analyzed the 2008 Wenchuan earth-

quake videos and discovered that the relationship be-

tween arrival time and order of evacuee arrival is lin-

ear in evacuation drills but nonlinear in real evacua-

tions (Yang et al., 2011).

D’Orazio et al. discovered several unique evac-

uation behaviors in earthquakes through video anal-

ysis, e.g., people preferred to search for safe posi-

tions instead of exits when the shaking was strong;

Haiti evacuees attempted to reach for exits but the

Japanese adopted drop-cover-hold on procedures and

preferred group evacuations; people attempted to

maintain social attachments during evacuations; peo-

ple tended to follow the common behavior; and peo-

ple stayed at safe and familiar places after the earth-

quakes (D’Orazio et al., 2014b).

Gu et al. examined school students’ evacuation

behaviors in videos and stated that the response time

was linear in normal conditions but nonlinear in real

evacuations, and the cumulative number of evacuees

was linear in exercises but nonlinear in real situations

(Gu et al., 2016).

Hori analyzed videotapes and estimated the walk-

ing speed of individuals during earthquake evacua-

tions (Hori, 2018).

Bernardini et al. studied whether evacuees fol-

lowed the recommended evacuation actions during

and after earthquakes using a video database. They

compared the response of people in New Zealand,

Italy, and Japan (Bernardini et al., 2019).

Several attempts have been made to build earth-

quake evacuation simulations and subsequently pro-

pose human behavior models for evacuations. Li et

al. developed a method for predicting the number of

casualties in earthquake evacuations using a cellular-

automaton-based evacuation behavioral model in con-

junction with a ﬁnite-element-based environment

model. The evacuation behavioral model was veriﬁed

by a real-life video recording of classroom evacuation

of Mingshan high school (Li et al., 2018).

Many researchers have adopted agent-based mod-

els to represent human evacuation behaviors. Del-

cea et al. developed an agent-based model for class-

room evacuation behaviors using NetLogo (Wilen-

sky, 1999) to analyze the differences in evacuation

processes between collaborative classrooms and tra-

ditional classrooms. The model was veriﬁed against

data acquired from evacuation experiments with 18

participants (Delcea et al., 2020).

Osaragi et al. constructed an agent model incorpo-

rating a procedure concerning typical evacuation be-

haviors such as initial reactions, searching for routes,

methods of evacuation, walking in a crowd, and activ-

ities while waiting. The model was employed to de-

velop efﬁcient disaster prevention planning (Osaragi

et al., 2012).

Mas et al. developed a tsunami evacuation simula-

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

tion model based on the Great East Japan Earthquake

to evaluate evacuation feasibility and shelter demand

analysis. The agent model consists of four layers:

evacuation decisions, shelter selections, route ﬁnd-

ings, and speed adjustments (Mas et al., 2012).(Mas

et al., 2012).

D’Orazio et al. identiﬁed numerous unique evac-

uation behaviors (rules) through video analysis. They

then combined these rules into behavioral ﬂowcharts

which were incorporated into the conduct of the

agents in their simulation models. Agent behaviors

were based on the intentional model, which represents

interactions among agents and between an agent and

the environment, and the social force model, which

represents the internal factors motivating the agents

(D’Orazio et al., 2014a; D’Orazio et al., 2014b;

Bernardini et al., 2014).

Most studies focused on ﬂeeing behaviors during

evacuations, and a few studies mentioned the drop-

cover-hold on action (D’Orazio et al., 2014b; Bernar-

dini et al., 2019). In this study, by analyzing videos

captured during the Great East Japan Earthquake,

we investigated evacuees’ choice of decision between

performing the drop-cover-hold on action or ﬂeeing

from a room.

3 VIDEO ANALYSIS

The video frame as shown in Fig. 1 was captured

in a hotel meeting room in Miyagino-ku, Sendai-shi,

Miyagi Prefecture, Japan, at 14:46 JST on Friday,

March 11, 2011

. The behavior of 48 people in the

room was recorded during the Great East Japan Earth-

quake, an earthquake off the coast of Japan with a

magnitude of 9.0. We carefully traced and summa-

rized the behavior of each person shown in Fig. 1b-d.

Fig. 2 depicts the initial positions of all 48 peo-

ple when the earthquake started. The room is square

with only one exit in the lower right corner. Initially,

all 48 people were sitting at tables in a square, with

everyone facing inwards. After the shaking from the

earthquake started, the following three behaviors were

observed in the video:

1. Stand and remain at their current position (Stand)

2. Exit and ﬂee from the room (Flee)

3. Hide under the table, also known as “drop, cover,

and hold on” (Drop)

Fig. 1d depicts the temporal behavioral changes for

each of the 48 people for 99 s after the shaking started.

Although the entire video lasted 139 s, the ﬁnal 40 s of

https://www.youtube.com/watch?v=tejlDDKeg8s

the video was dark owing to a power outage; hence,

analysis was difﬁcult. In the chart, the gray, white,

and black circles indicate stand, ﬂee, and drop behav-

iors, respectively, while × indicates behavior uniden-

tiﬁable from the video. Fig. 1c depicts the number of

times that people changed their behaviors. For exam-

ple, a person changing his/her behavior from sitting

to ﬂeeing is indicated by a white circle at the time the

behavior changed. Fig. 1b shows a cumulative curve

of the number of behavioral changes in Fig. 1c. The

dotted, dashed, and solid lines indicate drop, ﬂee, and

the sum of both behaviors, respectively. Figure 1a de-

picts the acceleration generated by the earthquake in

the North-South, East-West, and up-down directions

Two vertical dashed lines at 28 and 68 s indicate the

two peaks of shaking intensity.

Fig. 1d indicates the time when each person

changed their behavior. At 4 s, person 46 was the ﬁrst

to take action (ﬂee). Person 29 remained in his/her

chair for 75 s, and then changed his/her behavior to

stand. As shown in Fig. 1b, the cumulative num-

ber of people who displayed evacuation behaviors in

real disasters follows a convex curve, as compared

with the linear trend exhibited in evacuation drills (Gu

et al., 2016).

The ﬁnal behavior of all 48 people at 99 s is shown

in Fig. 3. A white circle with a black number de-

notes an evacuee who selected the ﬂee behavior, and

a black circle with a white number denotes an evacuee

who selected the drop behavior. A gray number is an

evacuee whose behavior is unknown. In summary, 26

evacuees selected ﬂee and 12 evacuees selected drop.

Fig. 3 illustrates that most people close to the exit

selected ﬂee, while those farther from the exit se-

lected drop, which is an intriguing behavior never re-

ported previously. The boundary between ﬂee and

drop crosses the room diagonally, as if people within

a certain distance from the exit selected ﬂee and the

others selected drop, thus leading to the following hy-

pothesis.

Hypothesis 1. The evacuation decision between ﬂee

or drop is based on the distance from the exit.

However, a different hypothesis for the phenomenon

can be constructed. That is, although people make ar-

bitrary decisions between ﬂee and drop, herd behavior

is the basic mechanism forming this diagonal spatial

pattern, as it is often observed in collective evacua-

tions.

Hypothesis 2. The herd behavior among people

causes the diagonal spatial pattern, even if each in-

dividual randomly chooses to ﬂee or drop.

https://www.data.jma.go.jp/svd/eqev/data/kyoshin/

jishin/110311 tohokuchiho-taiheiyouoki/index.html

(Observation Point: Sendai-shi, Miyagino-ku, Gorin).

Validation of Evacuation Decision Model: An Attempt to Reproduce Human Evacuation Behaviors during the Great East Japan Earthquake

(a) Earthquake accelerations.

(b) Cumulative person behavioral changes.

(d) Per person behavioral history.

Figure 1: Temporal behavioral changes in 99 s, of 48 people in the video.

It is obvious that Hypothesis 1 can generate the diago-

nal spatial pattern. However, this hypothesis requires

higher level cognitive processes such as rules, scenar-

ios, or procedures to estimate the distance to the exit

and to judge whether this distance is above a certain

threshold. By contrast, Hypothesis 2 requires only

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

Figure 2: Initial location of the people in the video.

Figure 3: Selection of drop and ﬂee at the end of the video.

lower-level cognitive processes, i.e., the herd behav-

ior, which is typical in many organisms.

In this study, we show that the evacuation deci-

sion model can represent human evacuations in a real

disaster situation by demonstrating that Hypothesis 2

holds using multiagent simulations, i.e., the simple

herd behavior is sufﬁcient to produce the diagonal

spatial pattern generated by the ﬂee and drop deci-

sions.

4 EVACUATION DECISION

MODEL

In this study, we adopted the evacuation decision

model (Tsurushima, 2019a; Tsurushima, 2019b) to

represent the herd behavior among evacuees during an

earthquake. The evacuation decision model is based

on the response threshold model in biology, which

represents the division of labor in eusocial organisms

(Bonabeau et al., 1996; Bonabeau et al., 1998). The

evacuation decision model has been used to reproduce

cognitive aggregation (Tsurushima, 2019a) and sym-

metry breaking in exit selection (Tsurushima, 2019b;

Tsurushima, 2019c) during evacuations.

In the evacuation decision model, the environment

has an objective risk value r, which refers to the sever-

ity of disaster threats. An agent i in the environment

acts as either a leader or a follower, depending on

his/her internal mental state X. The agent will act as a

leader if X = 1, allowing him/her to select his/her be-

havior, while a follower’s (X = 0) behavior is selected

by the behaviors of others. The value of X changes

with some probability during evacuations. Thus, an

agent sometimes acts as a leader and at other times a

follower.

The agent has a parameter θ

, called the response

threshold, which determines the degree to which the

agent participates in the evacuation. The values of θ

vary by agent. The probability P that an agent be-

comes a leader per unit time is

(X = 0 → X = 1) =

+ θ

, (1)

where s

is the local estimation in the stimulus of the

environment associated with agent i.

The probability that an agent becomes a follower

per unit time is

(X = 1 → X = 0) = ε, (2)

where ε is a constant probability that agents become

followers, given as a simulation parameter. The esti-

mation of the stimulus of agent i per unit time is given

by the following difference equation

(t + 1) = max{s

(t) +

δ − α(1 − R)F, 0 }, (3)

where

δ is an increase of the stimulus per unit time

δ =



δ if r > 0

0 otherwise,

(4)

and α is a scale factor of the stimulus. R is the risk

perception function which is a function of the objec-

tive risk r:

R(r) =

1 + exp(−g(r − µ

))

, (5)

where g is the activation gain which determines the

shape of the sigmoid function. µ

is the risk perception

of agent i, which represents an individual’s sensitivity

to risk. The evacuation progress function i.e., the lo-

cal estimation of the evacuation progress of agent i

F(n) =



1 − n/N

max

n < N

max

0 otherwise,

(6)

Validation of Evacuation Decision Model: An Attempt to Reproduce Human Evacuation Behaviors during the Great East Japan Earthquake

where n is the number of agents in the vicinity, and

max

is the maximum possible number of agents in

the vicinity.

5 EARTHQUAKE EVACUATION

SIMULATION

This section presents the earthquake evacuation sim-

ulation in detail. The simulation conﬁguration is sim-

ilar but not identical to the video depicted in Figure 1.

The aims of this simulation are to reproduce the con-

vex curve of cumulative evacuees from Fig. 1b and

the diagonal spatial pattern of Fig. 3. To represent

herd behavior, the evacuation decision model is incor-

porated into the agents; however, the agents have no

higher-level cognitive processes to determine whether

the distance to the exit exceeds a threshold. The sim-

ulation model was implemented using NetLogo 6.0.2

(Wilensky, 1999).

5.1 Conﬁguration

In this study, 500 agents, A = {a

,... ,a

500

}, were

randomly distributed in a square room (40×40 units),

with the lower left corner as the origin and the

exit at the lower right corner. Assuming simula-

tion time t = 1,. .., T , an agent a

∈ A has coordi-

nates x

(t),y

(t) ∈ R, a local estimation of the stim-

ulus s

(t) ∈ R, a mental state X

(t) ∈ {1, 0}, and an

action π

(t) ∈ {undecided, ﬂee, drop}, where unde-

cided indicates that the agent has not determined an

action yet, ﬂee implies that the agent has selected

Flee, and drop means that the agent has selected drop.

Furthermore, an agent has two parameters; the re-

sponse threshold θ

and the risk sensitivity µ

. Let

(1),y

(1) ∼ U (3,38), s

(1) = 0,X

(1) = 0,π

(1) =

undecided,θ

∼ U(0,100), and µ

∼ U(0,100) be the

initial values of the simulation, with the simulation

terminated at T = 270.

The vicinity of a

is deﬁned as V

= {a

∈

A | ν(a

)}, where ν : A

→ {true, false}, and ν

refers to the range of ﬁve units and 120

◦

towards the

direction of motion of a

It is noteworthy that n in Equation 6 will be n =

|{a

∈ V

| π

(t) = undecided}|, because both ﬂee and

drop are considered as evacuation behaviors in our

simulation.

At each time step, an agent with π

(t) = ﬂee moves

toward the exit (G

, G

) by ∆x, ∆y, as determined by

solving Problem 1.

Algorithm 1: Leader.

if π

(t) = undecided then

τ ∼ U(0,1)

if τ ≤ 0.5 then

(t) ⇐ drop

else

(t) ⇐ ﬂee

end if

if π

(t) = ﬂee then

Solve Problem 1 and determine ∆x, ∆y

(t) ⇐ x

(t) + ∆x; y

(t) ⇐ y

(t) + ∆y

end if

Algorithm 2: Follower.

⇐ |{a

∈ V

| π

(t) = drop}|

⇐ |{a

∈ V

| π

(t) = ﬂee}|

⇐ |{a

∈ V

| π

(t) = undecided}|

if n

> n

and n

> n

then

(t) ⇐ drop

else if n

> n

and n

> n

then

(t) ⇐ ﬂee

end if

if π

(t) = ﬂee then

Solve Problem 1 and determine ∆x, ∆y

(t) ⇐ x

(t) + ∆x; y

(t) ⇐ y

(t) + ∆y

end if

Problem 1.

min (x

(t) + ∆x − G

)

+ (y

(t) + ∆y − G

)

(7)

s.t. ∆x

+ ∆y

= 1 (8)

An agent with π

(t) 6= ﬂee remains in the same posi-

tion, i.e., ∆x = 0 and ∆y = 0.

When X = 1, a

acts as a leader by executing Al-

gorithm 1 and executes Algorithm 2 as a follower if

X = 0. The followers only mimic the most popular

behavior in their vicinity. Only the leaders intention-

ally determine their behaviors, albeit a random choice

between ﬂee and drop.

The room has an objective risk that starts at r(1) =

0 and increments by one for each time step up to the

maximum value of r(t) = 100. The local estimation of

the stimulus of a

starts at s

(1) = 0 and increments by

δ at each time step as F ≈ 0 in the initial stages of the

simulation. Thus, P

(X = 0 → X = 1) increases grad-

ually depending on θ

, thus resulting in the emergence

of leader agents. Subsequently, followers appear and

herding spreads among the agents.

Finally, the overall simulation procedure is shown

in Algorithm 3. The following parameters were used:

α = 1.2, δ = 0.5, ε = 0.2, g = 1.0, and N

max

= 10.

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

Algorithm 3: Simulation.

Initialization

for t = 1 to T do

r ⇐ min{r + 1, 100}

for all a

∈ A do

Calculate R {Equation 5}

Calculate F {Equation 6}

Calculate s

{Equation 3}

τ ∼ U(0, 1)

if τ < P(X = 1 → X = 0) then

⇐ 0

else

τ ∼ U(0,1)

if τ < P(X = 0 → X = 1) then

⇐ 1

end if

if X

= 0 then

Execute Algorithm 2 {Follower}

else if X

= 1 then

Execute Algorithm 1 {Leader}

end if

if (x

(t) − G

)

+ (y

(t) − G

)

< 1 then

A ⇐ A \ a

end if

end for

5.2 Result 1

In this section, results of the simulation described in

Section 5.1 are presented.

Fig. 4 depicts the cumulative curves of evac-

uees who performed evacuation actions per simula-

tion time. The solid line in the chart is obtained by

the function ϕ(t):

ϕ(t) = ϕ(t − 1) +

∑

∈A

(t), (9)

where

(t) =







1 t = min{z | π

(z) 6= undecided}

0 otherwise,

(10)

and ϕ(0) = 0.

The function ϕ(t) illustrates the temporal change

in the cumulative number of evacuees who performed

either the ﬂee or drop action. Similar to Fig. 1b, the

cumulative curves in Fig. 4 are convex, which is typi-

cal for actual disaster evacuations (Gu et al., 2016).

Fig. 5 illustrates the location of the remaining

agents in the room at t = T . A total of 211 agents

remained in the room, and all of them were with

(T ) = drop. Interestingly, all of the remaining

agents were located in the far side of the room from

the exit, in a triangular shape with a diagonal bound-

ary across the room. As the side close to the exit was

empty, it could be inferred that the agents farther from

the exit performed the drop action and those closer

performed the ﬂee action. This result is consistent

with the observation of Fig. 3.

If we divide the room into two spaces with a di-

agonal line y = x, and let the number of agents in the

upper left space be N

= |{a

| y

(T ) ≥ x

(T )}| and

the lower right space be N

= |{a

| y

(T ) < x

(T )}|,

we have N

= 183 and N

= 28, and the difference

= N

− N

= 155.

Here, we adopted entropy to evaluate the simu-

lation results quantitatively (Crociani et al., 2016).

Fig. 6 depicts each agent’s action at the end of the

simulation at their initial location t = 1. The black and

white circles represent π

(T ) = drop and π

(T ) = ﬂee,

respectively. As shown in Fig. 6, with a few excep-

tions, most agents initially located in the upper left

space selected drop and those in the lower right space

selected ﬂee. To evaluate whether the decision be-

tween ﬂee and drop is divided by the diagonal line

y = x, entropy H is introduced.

H = −r

log

) − r

log

), (11)

where

= L

/(L

+ L

) (12)

= L

/(L

+ L

), (13)

and

∑

(1)≥x

(1)∧π

(T )=drop}

+ (14)

∑

(1)<x

(1)∧π

(T )=ﬂee}

(15)

∑

(1)≤x

(1)∧π

(T )=drop}

+ (16)

∑

(1)>x

(1)∧π

(T )=ﬂee}

, (17)

where



(1) − y

(1)



(18)

and l

is the minimum distance between the initial

position of a

and the diagonal y = x. L

is the

sum l

of the shortest distances to the diagonal from

the initial position of a

in the upper left space with

(T ) = drop and an agent in the lower right space

with π

(T ) = ﬂee. Conversely, L

is the sum of l

Validation of Evacuation Decision Model: An Attempt to Reproduce Human Evacuation Behaviors during the Great East Japan Earthquake

Figure 4: Cumulative number of evacuees over the simulation time.

Figure 5: Distribution of re-

maining agents in the room

at the end of the simulation.

Figure 6: Initial locations

and the decisions between

ﬂee and drop. Black cir-

cles refers to π

(T ) = drop,

and white circles refers to

(T ) = f lee.

Figure 7: Calculations of L

and L

. In this case, L

+ l

and L

= l

+ l

of the shortest distance to the diagonal from the ini-

tial position of agent a

in the upper left space with

(T ) = ﬂee and an agent in the lower right space

with π

(T ) = drop. For example, in the case of Fig. 7,

= l

+ l

and L

= l

+ l

With a smaller H, ﬂee and drop behavior are de-

lineated by the y = x diagonal, whereas the behavior

becomes intermingled if H is close to 1.0. The en-

tropy in Fig. 6 is H = 0.48.

5.3 Result 2

A total of 150 simulations were conducted and the

results are presented in this section. Fig. 8 shows the

mean value, Φ(t) = ϕ(t), of the cumulative number of

evacuees over 150 simulations. The curves in Fig. 8

are convex and smoother than those in Fig. 4.

The histograms of N

and H are shown in Fig. 9

and Fig. 10, respectively. N

is the difference in the

number of agents in the spaces, separated by the di-

agonal y = x and H is the entropy of the evacuation

decisions. The mean, standard deviation (σ), and min-

imum and maximum values of N

and H are summa-

rized in Table 1.

Table 1: Statistics of N

and H over 150 simulations.

min mean σ max

-89.0 95.2 48.8 177

H 0.35 0.63 0.13 1.00

The kernel density distribution given by coordi-

nates x

(T ), y

(T ) of the agents remaining in the room

at t = T over 150 simulations is shown in Fig. 11. Lo-

cations farther from the exit exhibit a higher kernel

density, yielding a correlation between the distance

from the exit and the number of the remaining agents.

6 ANALYSIS

In this section, we analyze the relationship between

the ﬁnal agent decisions and the agent parameters us-

ing logistic regression analysis. The objective vari-

able is the ﬁnal decision π

(T ) by assuming drop = 1

and ﬂee = 0, and the explanatory variables are the dis-

tance between the initial agent location and the exit

(1) − G

)

+ (y

(1) − G

)

, (19)

response threshold θ

, and risk sensitivity µ

A total of 200 and 500 training and test samples,

respectively, were randomly selected from 75,000

samples (500 agents × 150 simulations). Large

amounts of training data reduce both the p-value and

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

Figure 8: Mean values of ϕ(t), Φ(t), over 150 simulations.

Figure 9: Histogram of

Figure 10: Histogram of

Figure 11: Kernel density

of x

(T ) and y

(T ) over the

room.

Figure 12: Logit model

discriminating between

drop and ﬂee.

Table 2: Results of the logistic analysis. Coefﬁcients and

P-values.

Intercept L

coeff -3.4486 0.0033 0.0115 0.0055

P-values 0.0 0.0 0.0796 0.3532

the reliability of the analysis; therefore, we set the

amount of training data to 200 samples. The results

of the logistic regression analysis in terms of the co-

efﬁcients and p-values are shown in Table 2.

From Table 2, the primary factor in the drop/ﬂee

decision is the distance to the exit (p < 0.01). This

implies that the response threshold θ

may have some

effect (p < 0.1), while the risk sensitivity µ

may not

have an effect on the decision (p > 1).

The box plot in Fig. 12 shows the discrimination

results of 500 test data samples by the logit model

from Table 2 developed from the logistic regression

analysis. It indicates that the logit model can discrim-

inate between drop and ﬂee decisions for unknown

data.

7 DISCUSSION AND

CONCLUSION

By analyzing a video captured during the Great East

Japan Earthquake, we discovered that the decision be-

tween drop and ﬂee was inﬂuenced by the distance to

the exit, a ﬁnding that was not reported previously.

Subsequently, we constructed two hypotheses for the

origin of this behavior and demonstrated that the spa-

tial pattern of the decisions could be reproduced. Fur-

thermore, Hypothesis 2 holds based on simulations

using the evacuation decision model, which repre-

sents human herd behavior during evacuations.

Our simulation results in Figs. 5 and 6 show that

the decision between drop and ﬂee is determined by

the distance from the exit. However, Figs. 9 and 10

indicate that the results from the simulation vary, i.e.,

the results described in Section 5.2 are not always ob-

tained. Contrary to our expectations, some simula-

tions had N

= −89, signifying that more agents re-

mained in the area closer to the exit, and some simu-

lations resulted in H ≈ 1.00, which implies a combi-

nation of drop and ﬂee behaviors.

Although we concede that exceptional cases like

these occur, it is clear that the evacuation decision

model is sufﬁcient to produce results that are similar

to our ﬁndings from the video analysis of the Great

East Japan Earthquake, albeit at a slightly lower fre-

quency. The kernel density of the results in Fig. 11

agrees well with the statement above. Furthermore,

the results of the logistic regression analysis revealed

that the primary factor of deciding between drop and

ﬂee for individual agents was the distance between

their initial location and the exit. Nonetheless, Hy-

pothesis 2 suggests that each individual does not have

to consider this distance to determine his/her behav-

ior; a simple herd behavior is sufﬁcient to produce the

diagonal spatial pattern.

The most remarkable aspect of this analysis was

that the results were produced by agents who have no

higher-level cognitive processes. The agents in our

model performed only either imitations or random se-

lections, both of which are unintelligent behaviors.

Validation of Evacuation Decision Model: An Attempt to Reproduce Human Evacuation Behaviors during the Great East Japan Earthquake

Neither distance estimation nor thresholds are neces-

sary to reproduce the behaviors in the video. From

the discussion thus far, we conclude that Hypothesis 2

holds.

The fact that Figs. 4 and 8 are consistent with

Fig. 1b and that the cumulative curve of evacuees is

convex in real evacuation situations (Gu et al., 2016)

provides additional support that our simulations us-

ing the evacuation decision model can yield realistic

results.

We do not deny Hypothesis 1; rather, we consider

it natural for people close to the exit to select the ﬂee

action intentionally. In reality, we believe that both

Hypothesis 1 and Hypothesis 2 hold simultaneously.

A real evacuation process will be the complex com-

bination of higher-level cognitive processes such as

decision making and lower-level cognitive processes

such as herd behavior. Some researchers have also

pointed out the importance of individuals’ emotional

responses in crowd evacuation processes (Kefalas and

Sakellariou, 2017).

This study demonstrated that the evacuation de-

cision model could reproduce real human evacuation

behaviors that were recorded in the video of the Great

East Japan Earthquake and could be used to ana-

lyze human herd behaviors during earthquakes. Tsu-

rushima (Tsurushima, 2019b; Tsurushima, 2019c)

demonstrated that a simple herd behavior could re-

produce symmetry breaking in exit selection using the

evacuation decision model. Furthermore, the analysis

presented herein revealed the signiﬁcance of herd be-

havior in collective evacuations. Hence, the evacua-

tion decision model is advantageous for the quantita-

tive analysis of herd behavior effects in human evac-

uations.

Finally, some potential methodological weak-

nesses should be considered. First, the video clip an-

alyzed in this study is the only instance where we can

ﬁnd the speciﬁc evacuation behavior discussed in this

paper. We do not know the universality of this behav-

ior in other evacuation scenarios. There is also some

possibility of errors occurring in the video analysis

phase as it was controlled manually. Second, most of

the parameters adopted in the simulation model were

either experimentally or arbitrarily selected. Sensitiv-

ity analysis of these parameters is desirable to ensure

more accurate future results. Third, the simulation

was not conﬁgured identically with the actual events

captured in the video. It would be interesting to ex-

periment with a more realistic design. However, such

considerations are reserved for future work.

ACKNOWLEDGEMENTS

We gratefully acknowledge the help and construc-

tive comments provided by Yoshikazu Shinoda and

Kei Marukawa. We would like to thank Editage

(www.editage.com) for English language editing.

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Validation of Evacuation Decision Model: An Attempt to Reproduce Human Evacuation Behaviors during the Great East Japan Earthquake