Journal of JSCE, Vol. 3, 209-223, 2015
(Originally published in Journal of Japan Society of Civil Engineers, Ser. B1, Vol. 69, No. 1, 71-82, 2013 in Japanese)

SCENARIO ANALYSIS FOR EVACUATIONSTRATEGIES FOR RESIDENTS IN BIG CITIESDURING LARGE-SCALE FLOODING
Toshitaka KATADA1, Noriyuki KUWASAWA2, Satoru SHIDA3 and Masaru KOJIMA4
1Member of JSCE, Professor, Research Center for Disaster Prevention in the Extended Tokyo Metropolitan Area,

Gunma University (1-5-1 Tenjin-cho, Kiryu-shi, Gunma 376-8515, Japan)
E-mail: katada@gunma-u.ac.jp
2Member of JSCE, Research Center for Disaster Prevention in the Extended Tokyo Metropolitan Area,
Gunma University (IDA Co., Ltd.) (1-5-1 Tenjin-cho, Kiryu-shi, Gunma 376-8515, Japan)
E-mail: kuwasawa@ce.gunma-u.ac.jp
3Member of JSCE, River Planning Division, Water and Disaster Management Bureau, Ministry of Land,
Infrastructure, Transport and Tourism (1F, Joint Government Building (Chuo Godo Chosha) No. 3,
2-1-3 Kasumigaseki, Chiyoda-ku, Tokyo 100-8918, Japan)
E-mail: shida-s2jf@mlit.go.jp
4Member of JSCE, River Department, Kanto Regional Development Bureau, Ministry of Land, Infrastructure,
Transport and Tourism (16F, Saitama New Urban Center Joint Government Building No. 2, 2-1 Shintoshin,
Chuo-ku, Saitama-shi, Saitama 330-9724, Japan)
E-mail: kojima-m8314@ktr.mlit.go.jp

In major cities, large populations can hinder evacuation efforts in the event of a large-scale flooding.
Therefore, for investigating evacuation strategies during large-scale flooding in such cities, population size
and its impact must be considered. This study aims to examine the evacuation problems that arise when big
cities are subjected to large-scale flood damage. In addition, it attempts to examine strategies that can overcome the evacuation problems. In this study, we developed a scenario simulator to elaborately simulate theflooding, evacuation of residents, and status of damage due to water exposure in the Edogawa Ward, which
has a population of approximately 650,000. In addition, using a simulation that reflected the intended evacuation behaviors of the residents for a scenario in which the banks of the Arakawa River overflowed, we wereable to identify the inherent evacuation problems of the city, including the damage caused by the presence ofa large number of evacuees. Furthermore, we analyzed scenarios including measures to reduce the extent ofdamage. From this information, it became clear that countermeasures such as spatial and temporal dispersion of evacuees and a reduction in evacuation demand are necessary.
Key Words: scenario analysis, evacuation strategies, big cities, large-scale flooding, simulation

tion and industries in such cities has also accelerated.
Consequently, extensive damage by large-scale
flooding is now a real concern. Hence, development
of disaster-prevention measures targeting large-scale
flood damage in big cities has become an urgent
concern. In particular, demand has risen for promoting software-based countermeasures, including
evacuation strategies.
In April 2010, the Expert Panel on Large-Scale
Flood Disaster Countermeasures of the Central Disaster Prevention Council of the Cabinet Office maderecommendations to reduce damage from large-

1. INTRODUCTION
In recent years, the risk of flooding has grown because of the increased frequency of torrential downpours and stronger typhoons as a result of climatechange associated with global warming. Therefore,
the need for disaster-prevention measures targeting
flood damage has also grown. In large cities, in particular, the frequency of exposure to water has reduced as a result of aggressive flood control
measures and facilities implemented in recent years.
However, at the same time, concentration of popula-209

scale flooding in the Tokyo metropolitan area1). According to estimations by this Expert Panel, in thecase of flooding of the Tone River, which would
cause maximum damage to the Tokyo metropolitanarea, an area of approximately 500 km2 would besubmerged in water and 2.3 million people would be
exposed to flooding. The Expert Panel also indicated that evacuation policies must be examined basedon the characteristics of the city to reduce the extent
of damage. As highlighted in these recommendations and in a case involving the city of Nagoya,presented in the next chapter, evacuation strategies
in large cities pose a difficult set of problems because a large population is affected.
Based on an awareness of the abovementioned
problems, the aim of this study is to understand theevacuation problems that arise with large-scale
flooding in large cities, as well as to examine appropriate countermeasures for such problems. First, tospecifically identify the problems associated with alarge number of evacuees present in cities with large
populations, we developed a scenario simulator tosimulate the evacuation of local residents necessitatedby flooding in the Edogawa Ward of Tokyo, which
has a population of over 650,000 people. We thenused the developed simulator to analyze a scenariothat assumed a washout of the banks of the Arakawa
River. This information was then used to understandthe status of damage based on the behaviors of theresidents to examine measures to reduce damage, as
well as to verify the effectiveness of such measures.
Consequently, we were able to clarify the evacuation problems inherent to large cities where stereotypical evacuations cannot be promoted or derived,
as well as the directionality of the strategies thatshould be adopted.

in some areas, the river banks did not experience
washout, and fortunately, large-scale external flooding did not occur. During the largest recorded flooding of the Shonai River, the city of Nagoya issued
an evacuation advisory that targeted over a million
residents living in 181 school districts within thecity 25 minutes after the river had exceeded the water level, which was the standard for announcing anevacuation advisory. However, the number of residents who evacuated to facilities specified by thecity was limited to approximately 5,000 people.
Hence, based on the evacuation percentage, only avery limited evacuation could be performed2). If wealso consider the evacuees who fled to locations
other than those that were designated as evacuation
shelters, the actual number of evacuees was a little
higher. However, if the Shonai River had burst its
banks at that time, it is not hard to imagine thelarge-scale damage that would have occurred. However, on the other hand, if most residents who were
warned to evacuate had actually evacuated, an unprecedented large-scale flood evacuation would
have unfolded. In such a scenario, it is easy to imagine that the evacuation would have resulted in serious confusion. Furthermore, if large-scale external
flooding had occurred, we cannot deny the possibility that there would have been numerous casualties
among the evacuees.
As can be imagined from this case, the problemof evacuating residents from large cities is a complicated concern, which, depending on the presence ofa large population to be evacuated, cannot be resolved by merely promoting evacuation. Therefore,
to examine appropriate evacuation strategies forlarge cities, rather than statically considering theregional population as the scale for a maintenancetarget or an evacuation target, we must also address
problems that occur during evacuation, including
evacuation behaviors using the way people think
and act as dynamic elements that depend on the
progression of the situation and the acquisition of
information.

2. POLICY FOR EXAMINING FLOODING EVACUATION MEASURES FOR
LARGE CITIES(1) Evacuation problems during flooding of large
citiesWhen Typhoon No. 15 was located in the southern ocean waters of Kyushu the day before it madelandfall in eastern Japan and caused extensive damage in September 2011, it fed damp air to the stagnated front line on Honshu and caused intermittent,
heavy downpours in the Gifu and Aichi prefectures.
At the Shonai River, the watershed for this region,
the torrential downpour caused the water level torise rapidly, reaching a height of 6.23 m, thereby
exceeding the previous peak caused by the Tokai
torrential downpour in 2000 by approximately 50
cm. However, while excessive water was observed(2) Policy for examining the evacuation of residents in the event of large-scale flooding of alarge city
In this study, based on an awareness of the problems described in the previous section, we developed a scenario simulator (hereinafter, simulator) to
specifically determine the status of flood damage ina specific region to examine the problem of evacuation in the event of large-scale flooding in a metropolitan area.
Much of our nation’s simulation research targeting the evacuation of residents focuses on confined
spaces such as underground complexes3) or targets

210

only a portion of the given region4). There are only
few simulations that assume the evacuation of alarge population in a large city5), 6). However, local
municipalities are examining the evacuation of residents. In studies that target large-scale flood damagein which an extensive area is submerged in water, atthe very least, the evacuation conditions of the entire
area of each municipality must be considered. Furthermore, when a large city is affected, an examination specifically considering the impact of population size on the evacuation and devastation conditions is required. Other countries have also conducted simulation research that pertains to evacuationsin the event of flood damage over an extensive area.
This research can be classified into different types
such as research to determine the time required forevacuations based on the population and size of theaffected area, the attributes of its residents7), 8), predictions of time taken for evacuation using the numberof evacuees, the OD, the network of roads9), 10), andmicro-simulations that model the individual behaviorof the evacuees and agent-based modeling11), 12). Of
course, much of the evacuation responses that are
examined in these research areas focus on efficiently
evacuating residents to areas that are not submerged
by water, as typified by hurricane response in the
United States, and much of this research is not inaccordance with the evacuation policies of Japan,
which are based on municipality units. In addition,
as with this research, research that utilizes microsimulation models to determine individual evacuation conditions focuses on evacuation by car. In fact,
there is no simulation research that specifically andcomprehensively models the conditions of a region
in the event of a flood, such as the conveyance ofdisaster-related information to residents, the behavior of evacuees including walking, and the dynamic
relationship with flooding.
From an awareness of such problems and fromprevious research, in our study, we considered thefollowing policies as we developed a simulator andconducted the examination. We targeted an entire
large city and specifically aimed at modeling largescale evacuation based on the intended behaviors of
its residents.
● To identify the specific problems with residential
evacuations, the simulator was designed to not
only model hazards that pertain to flooding disasters, but also to comprehensively model every
aspect, including evacuation behaviors and othersocial response behaviors.
● From information obtained by an examinationthat targets a large city, the simulation models
the impact that the population scale has on damage and evacuation behavior of residents.
● To conduct an examination based on the charac-

teristics of the targeted region, the simulator
elaborately models the elements that express theaffected region such as its topography, road network, distribution of residents, and establishment
and layout of disaster prevention facilities.
● The response behavior of residents is an elementthat has a major impact on the extent of damage,and therefore, the simulator reflects and models
the residents’ level of awareness to examine actual issues and strategies.
● To examine strategies that aim toward reducing
human suffering, the priority of measures withdifferent direct goals, such as improving information transmission and providing evacuation
support, must be examined. Therefore, the simulator models human suffering, and uses the reduction numbers as common evaluation indicators for different strategies.

3. BUILDING THE SIMULATION MODEL
In this study, to develop a simulator in accordance with the policies mentioned in the previous
chapter, we used comprehensive disaster scenario
simulators13), 14) that we had developed previously asa basic model. The basic model was configured using three different models, namely, an informationtransmission model to express the status of transmitting disaster-related information, an evacuation behavior model to express the evacuation conditionsof the residents, and a flooding model to express thehazard conditions. In this study, for the evacuation
behavior model, reduction in evacuation speed inaccordance with the extent of traffic jams and thelevel of congestion caused by evacuees on foot wasconsidered, and improvements were made toachieve modeling with a more accurate representation of the evacuation speed and the evacuation behavior in a large city.
(1) Simulator configuration
To model the evacuation conditions of residents
in the event of large-scale flood damage, a simulator
configured from the computational models shown in
Fig.1 was developed. These computational models
can be largely classified as models that express hazards related to flood disasters, models that express
social response in the event of a flooding disaster,and models that estimate human suffering. The
computational model that expresses hazards comprises a river model that expresses the change inwater level of the rivers and a floodplain model that
expresses the water depth and area in a flooded region within a dike area. On the other hand, the so-

211

Hazard Phenomenon
River
Model

Water
Level

River
Water
Level

Flood Region,
Water Depth,
Flow Speed

Information DisasterTransmission Information
Model

Social Response

Floodplain
Model

Evacuation
Behavior
Modelflowing into a dike region due to a dike break or
overflow. The dike break flow rate or overflow rate
due to river flooding is calculated by the Honma
lateral overflow formula16) based on the calculated
mesh water level of the floodplain, corresponding to
the water level in accordance with the river model,
and the relationship with the dike break height andembankment level height. Note that with this simulator, the flooding conditions within the dike area
must be evaluated in conjunction with the evacuation behavior of the residents, and therefore a 10 m
detailed computational mesh is used.
c) Information transmission model
The information transmission model expresses
conditions surrounding the transmission of warnings,
evacuation advisories, and other disaster-related information to the residents through radio communications for disaster prevention, mass media, andother such means. Moreover, to model the conditions for which information is disseminatedthroughout the region by word of mouth among residents, the individual behavior of residents with respect to conveying information is also considered.
To implement this model, we used a disaster information transmission simulation model17) that wedeveloped in a previous research. The disaster information transmission simulation model consists ofparameters that handle the level of informationtransmission behaviors between residents. Moreover,it can model the conditions of transmission behavior
according to measures such as the cohesiveness of
the local community at the time of a disaster. Thismodel demonstrates information transmission in theevent of a disaster by radio communications for disaster prevention and by the media based on a modelof information transmission behavior of residents.
d) Evacuation behavior model
The evacuation behavior model primarily expresses the conditions of movement of the residents
from their homes to their evacuation destinations via
roadways. The timing at which evacuations begin isbased on the timing at which such as evacuation
advisories are acquired as modeled by the information transmission model. It includes the timespent on evacuation preparations. Furthermore, forthe evacuation destination and route, as a generalrule, the evacuation facility closest to the resident’s
home and the shortest route to that facility are used.
Note that the modeling of this portion is describedin detail in the next chapter.
During an evacuation, the position of an evacuee
is determined from the time that has lapsed sinceevacuation first started, as well as roadway data inaccordance with the travel speed. The roads of atargeted region are expressed as a network composed of links that express roads and nodes that ex-

Flood Region,
Water Depth,
Flow Speed
DamageOccurrence
Model
Residential
Distribution

Key

Computational
Model
Reference
Information

Fig.1 Configuration of the simulation model.

cial response model comprises an informationtransmission model that expresses the conditions
surrounding the transmission of disaster informationby the government to the residents in accordancewith the hazard conditions, and an evacuation behavior model that expresses the decision by residents to evacuate based on the acquisition of disaster-related information and the conditions surrounding their movement to evacuation facilities. Furthermore, a damage occurrence model is used toestimate the scale of human suffering from the results output by the other two model groups.
(2) Overview of the simulation modelThis section is a general overview of each computational model presented in Fig.1. Note that eachmodel basically utilizes existing technology, andtherefore, the details are limited to the information
presented by the documents listed in the references.
a) River model
The river model expresses temporal changes inwater levels and flow rates in river channels using
the tidal level at the river mouth as the starting waterlevel and the hydrograph for the flow rate applied at
the upstream end as a boundary condition. Because
the temporal changes in flooding conditions are expressed with this model, one-dimensional unsteadyflow calculation15) is used as the computational
method. In addition, with this model, points for calculating the flow rate and flow speed are placed in atransverse cross-section of the river channel to establish the river width and hydraulic radius, and calculations of the water level are performed using anintermediate point in this cross-section. If an area of
convergent flow exists in the targeted region, theflow rate at the downstream end of the area of convergent flow is calculated using a common waterlevel calculation point for a downstream crosssection of the branch streams and a common waterlevel calculation point for the area of convergent
flow in the given river.
b) Floodplain model
The floodplain model is expressed by Cartesian
coordinate system-based two-dimensional unsteadyflow calculations15) of the behavior of floodwaters212

press intersections. Each link has a changeable condition that expresses whether or not passage is possible at any given point in time. If a link that anevacuee tries to enter is not passable, the unpassablelink is excluded from the network, and route searches that use the resulting network are implemented todetermine the appropriate detour. Moreover, theflooding conditions expressed by the floodplain
model are reflected every 10 seconds to update theroad conditions, and the emergence of unpassable
locations in conjunction with the spread of theflooded area and the detour behavior of evacuees inaccordance with such are also modeled14).
Both walking and the use of automobiles aremodeled as evacuation measures. In the event offlood damage, evacuation is done by walking as a
general rule, but for reasons such as vehicles being avaluable asset and movement by walking during atorrential downpour being difficult, evacuation by
car is widely used18). Travel speed is based on anarbitrarily established speed. However, regarding
the speed of vehicles, when an evacuee attempts toenter a link and the traffic volume of a link on
which an evacuee is currently traveling changes, thechange in speed is expressed in accordance withtraffic volume by updating the speed using Greenshields19) equation.
vc = vf · (1 – k ⁄ kj)

lated using the surface areas of the sidewalks andthe number of evacuees in the given link. If 6 ≤ d, it
was assumed that evacuees could not enter the given
link from a separate link. Furthermore, if the evacuation took a long time, a reduction in speed due tofatigue of the residents evacuating by walking isconceivable; therefore, the speed reduction due tofatigue was considered using equation (3) 21).
rf = 1 ⁄ (0.982 + e1.12t − 4)

where rf is the speed coefficient due to fatigue and t
is the amount of time (hours) that has lapsed since
the evacuation started. A reduction in speed inflooded areas was also considered using equation(4) 22).
rw = 1 – w ⁄ wmax

(1)

where vc is the vehicle speed (km/h), vf is the freetravel speed (km/h), k is the traffic density (cars/km),
and kj is the saturation density (cars/km, set to 140
for calculations for this study). Free travel speed isbased primarily on the scale of the roads, and therefore, in this study, expressways were set to 30 km/h
and all other roads were set to 20 km/h based on
surveys, such as a road traffic census of the targeted
region. Moreover, traffic density was determinedusing an inverse number of the spacing between thefront of the vehicle and the vehicle ahead. With thismodel, vehicle lanes and traffic signals were omitted
to simplify the model, and priority was given tothrough-traffic at intersections. Because of evacuation behaviors in large cities, congestion resulting
from pedestrian evacuees occurs; therefore, reduction in the evacuation speed must also be considered.
Therefore, the following equations were used withreference to previous studies20) to model the reduction in walking speed in accordance with the levelof congestion.
rd = 1rd = 1.3 – 0.2d
rd = 0.1(d < 1.5)(1.5 ≤ d < 6)
(6 ≤ d)(3)(2)

where rd is the speed coefficient in accordance withthe level of congestion, and d is the level of congestion (people/m2). The level of congestion is calcu-

213

(4)

where rw is the speed coefficient due to flooding, w
is the depth of flood water (m), and wmax is the depthof flood water that is the limit for walking (m, set to
1 m for calculations during this study). While theimpact of flow speed should be considered with respect to decreases in speed in accordance withflooding; but because it was difficult to model theflow direction and the impact from shallow water
depths, in this study, only water depths that had amore dominant impact were considered23).
The above coefficients relating to speed decrease
were considered, and the evacuation speed of pedestrian evacuees was expressed using equation (5).
vw = rd · rf · rw · vs(5)

where vw is the pedestrian evacuation speed and vs is
the initial value for pedestrian evacuation speed.
e) Damage occurrence model
In the case of flood damage due to river flooding,even if a resident is within the flooded area, the person will not necessarily become a victim. Therefore,
with this model, the flooded population was categorized in three categories, as shown in Table 1, basedon the evacuation conditions of the residents, thecharacteristics of their homes, and the flooding conditions. This information was used as evaluation
indicators for the scale of human suffering. With
this model, residents who were in a flooding situation that made it difficult for them to walk wereconsidered to be residents who required rescuing.
To determine walking difficulty, an equation wasused from Suga et al.23) that determined walking
difficulty based on body height and hydrodynamic
force.

Table 1 Classification of flooded population.

Person in danger within aflooded area
Person requiring emergency
rescueNational orcity road
Other raodr>Definition
A person who stays in his or her own home without evacuating, and who is not personally experiencing flooding but whose home is within aflooded area. (The ground surface of the person’shome is flooding.)
A person who has not evacuated or is in theprocess of evacuating, and while emergency
rescue is not necessary, the person is locatedwithin flood waters.
A person who has not evacuated or is in theprocess of evacuating, is surrounded by flood
waters to the extent that walking is difficult, andan emergency rescue is necessary.

Edogawa Ward

Sidewalk width of 3.5 m

Rive

Ichikawa City

Shelter
Regional disasterprevention centeraPerson staying
within a flooded
area

Expressway

Ed
og
aw

Flooded Population

Ichikawa City
Urayasu City

A ra
ka
wa

Katsushika Ward

0

1

2

Riv
er
>

km

Koto Ward

Fig.2 Region targeted by the study.
Table 2 Calculation conditions.

4. ESTABLISHMENT OF THE BASICSCENARIOClassification

Item

Settings
200-year probableflooding

Design flood

In this chapter, we summarize the region targetedby the study and the calculation conditions that were
established (see Fig.2 and Table 2).

Flooding

Assumedflooding
Starting
water level
Dike breakareas

AP + 2.2 m

High water level
Maximum damage

LayoutAudio range
Hearing rate
NumberSpeed
Audio Range
Hearing rate

Arakawa River
left bank 7.0k
651,733 people
303,245 households
Walking speed
based on sex andage
256 speakers
280 m
30 %
10 cars
20 km/h
80 m
30 %Communication method

By word of
mouth only

Activitylevel
Sidewalkwidth

Not aggressively
communicatedWard documents
Ordinary value
Previous studies
Ward documents
Ordinary value
Ordinary value
Ordinary value
Phones cannot
be used due tocongestion.
Survey of residents

2 m, some 3.5 m

Ward documents

30 km/h on highways, 20 km/h on
all other road
106 shelter

Ward documents

Population
households(1) Establishing the calculation conditionsa) Targeted region
In this study, the Edogawa Ward of Tokyo wasselected to examine evacuation strategies in theevent of large-scale flooding. The Edogawa Ward islocated in the southeastern part of Tokyo and issandwiched between two major rivers, the Arakawa
River on the West and the Edogawa River on theEast. In addition, because the Edogawa Ward is located at a position furthest downstream from bothrivers, if the embankments of the rivers collapse, thegeographical features of the area are such that theward is at the risk of flooding regardless of where
the embankments collapse. Furthermore, because of
the effects of ground subsidence, 70% of the landarea is a zero-meter region below the high-tide level,and because there is no natural drainage, after flooding has occurred, an extensive area will be coveredin flood waters for an extended period of time withprolonged damage. Moreover, even within Tokyo,
the Edogawa Ward is a large, prominent city with apopulation of approximately 650,000 people, and if
flooding should occur, there are concerns that alarge population will need to be evacuated.
b) Conditions related to topography and population
First, a topographical map of the targeted region
was prepared with 10 m mesh precision using a laser profiler. Next, the distribution and shapes ofbuildings were modeled using commercially available electronic residential maps. The number of stories of each building was set using residential maps
and laser profiler data as a reference. Finally, network data that expresses the roadways was preparedusing the road shapes contained in the residential

Residents
Walking
speed
Loudspeakers
Publicity
cars
Communication
betweenresidents

Roads

Basic vehicle
speed

Shelters
Regional
Evacuation
disaster
facilities
prevention
centers

References andthinking

Ward documents

Statistical data

Ward documents

3 locations

maps as a reference.
First, to model the residents, we prepared virtual
residents, based on the demographic composition asof January 2010 that was announced by the Edogawa Ward. This data included population statistics by
district, age, and gender. Next, we targeted detachedhomes and apartment buildings shown in residential
maps by district, and randomly distributed virtual
households for the number of households that existed according to population statistics. We then distributed the population by randomly allocating theresidents prepared for each household premised on
the conditions that detached homes contained only asingle household.

214

Maximum Flood Water Depth
0.0 m - 0.5 m or less

Edogawa Ward
Assumed Location of Dike Breakage

0.5 m - 1.0 m or lessNo.
1

1.0 m - 2.0 m or less
2.0 m - 3.0 m or less
3.0 m - 4.0 m or less Shinnakagawa River >Kyuedogawa River >2

Over 4.0 m

3
4
5Arakawa River >
0

1

6

2
km

7

Fig.3 Assumed dike-breakage locations and maximum flood
water depth distribution.

89c) Conditions related to hazards
The river model was modeled based on the Arakawa River. For other rivers, topography data were
utilized to model full capacity conditions. The assumed flooding was set to the flooding levels of
September 1947 based on the design flood (1/200th
probability) of the Arakawa River. For the dikebreak, a 7.0 km location along the left bank of the
Arakawa River was selected, which is a location
with a relatively large population that would be affected by flooding within the targeted region. The
width of the dike break at the river embankment was
set to 340 m based on the dike failure results along
the Tone River caused by Typhoon Kathleen as areference. In addition, the timing of the dike break atthe embankment was set to the point in time atwhich a reference water level was reached with consideration of the current allowance height of theembankment and maintenance conditions. Flooding
calculations based on these conditions provided results that indicated that a large portion of the region
between the Arakawa River and the Shinnakagawa
River would be flooded (see Fig.3).
d) Conditions pertaining to disaster-prevention
facilities and disaster information
The evacuation facilities were modeled by targeting shelters such as elementary and middle schools,
and regional disaster-prevention centers (higher elevation parks) designated in the regional disasterprevention plan for the Edogawa Ward. When considering shelters, we referenced usable floor space,which excluded floors of buildings that were at risk
for flooding based on documentation from the
Edogawa Ward, and the number of people that canbe accommodated per shelter in an emergency evacuation was set based on two people per 1.65 m2 (thesize of one tatami mat). In addition, because the area
has considerable floor space in its regional disasterprevention centers to accommodate over two million
people, the number of people that can be accommodated in the centers was set as limitless. According
to the evacuation behavior model, if an evacuation
facility had exceeded its accommodation limit, theTable 3 Conditional stages during flooding
presented with the survey.
Conditions
Rain seems heavier than normal at home.
Heavy-rain warning or flooding warnings areannounced.
Warnings continue for a long time, and the rain does
not stop falling.
An evacuation advisory is announced.
Residents have become aware of conditions in which
the embankment is likely to collapse.
Evacuation instructions are announced.
Notification has been received that the embankment
collapsed.
Flooding reaches the area near a resident’s own home.
A resident’s own home begins to flood.

residents were evacuated from that location to thenext closest evacuation site.
Regarding radio communications for disaster prevention, the 256 outdoor loudspeakers prepared by
the ward were modeled. The rate at which information was acquired from the outdoor loudspeakers
was set to 30% based on a previous study24). Moreover, the timing at which the evacuation advisory wasissued and the timing at which evacuation instructions were issued were set as the point in time atwhich the water level at the Iwabuchi site of the
Arakawa River reached the water level determinedfor evacuation and when it reached a flooding risk
level, respectively. Both timings were established byreferencing the ward’s regional disaster prevention
plan. Consequently, with the assumed flooding scenario, an evacuation advisory was issued 4 hours
and 44 minutes prior to the collapse of the embankment, and the evacuation instructions were issued 2hours and 41 minutes prior to the collapse.
(2) Intentions of residents regarding flooding
evacuation and method for reflecting those intentions
In this section, we discuss the results of anawareness survey that was conducted to understandthe intended behavior of the residents in the event of
a flood disaster and the method that was used to reflect that behavior in the evacuation behavior model.
a) Method for reflecting the intentions of the residents and an overview of the survey
In this study, we decided to consider the intentions of the regional residents in three areas: (1) determination of intent to evacuate, (2) timing at
which evacuation began, and (3) behavior at thetime of evacuation. We expressed these intentions in
the evacuation behavior model. First, regarding thedetermination of intent to evacuate, we presented
the conditional stages during flooding, as shown in
Table 3, and identified the evacuation intentions of
the residents at each stage. Furthermore, because the215Table 4 Overview of the resident awareness survey.
Survey period
February 19–23, 2010
Residents 20-years-old or older of theSurvey target
Tokyo, Edogawa Ward
Survey method
Internet survey
No. of responses 3,000● Basic attributes● Awareness regarding flooding disSurvey items
asters● Behavioral intentions regarding
flooding disasters

Rate of Determination ofIntent to Evacuate (%)

100

When subjected topersuasion to evacuate.
(Includes people who
would evacuate
60 regardless of
persuasion.)
40
80

Residents home begins
to flood.

Flooding reaches area
near your own home.

Notified that the dikecollapsed.

Evacuation instructions
are announced.

You learn the dike islikely to collapse.

An evacuation advisory
is announced.

No.2 No.3 No.4 No.5 No.6 No.7 No.8 No.9

Warnings continue for along time, and the rain
does not stop falling.
Heavy rain or flooding
warnings areannounced.

216

No.1

Rain appears heavier
than normal.

0

impact of persuasion to evacuate is large25), we alsodecided to determine and understand the evacuation
intentions when residents were subjected to persuasion. Next, for the timing at which evacuation began,
we decided to add the time required for preparations
after the determination of intent to evacuate wasreached. Finally, regarding evacuation behavior, we
also decided to identify the evacuation methods anddestinations.
The abovementioned behavioral intentions of theresidents in the event of a flooding disaster were
determined using a survey targeting the residents ofthe Edogawa Ward. An overview of the survey ispresented in Table 4. This survey was implemented
using an online survey service, and responses wereobtained from 3,000 residents of the Edogawa Ward.
The survey asked questions regarding basic attributes, including questions regarding the presence ofany family members who would have difficulty inevacuating on their own and would require support.
To use the model to determine evacuation behaviors that reflected the intentions of the residents,
responses obtained in advance through the survey
were randomly selected for each household, andbehavior in line with the responses for each question
was then applied to the given household. Rather
than using the individual responses as is, anotherconceivable method was to model behavior basedon tallied results for each question. However, thismethod was not adopted because it did not allow for
expressing the correlation of the responses for eachquestion, and consistency in individual behavior
would be lost. Furthermore, differences were observed in the evacuation intentions between the residential areas (six districts) and the number of stories of the residence (single-family homes, singlestory apartment buildings, two-story or higher single-family homes, two-story or higher apartmentbuildings), and therefore, the responses were divided into groups according to these two attributes.
Moreover, when selecting the response to be associated with each household, the response was selectedfrom the group for which the household matched theresidential area and the number of stories of the residence.

When not subjected topersuasion to evacuate.

20

Fig.4 Rate of determination of intent to evacuate by conditions.

b) Determination of intent to evacuate
The percentages of people who decided to evacuate to a location outside of their homes at each of thestages, No. 1–9, as shown in Table 3, which wereobtained from a survey of the residents, and the percentages for a case in which the residents were also
subjected to persuasion are shown in Fig.4.
As shown in the figure, if we first focus on data
for when the residents are not subjected to evacuation persuasion, a large increase is observed at the
point in time that the residents learn about the evacuation advisory, but that percentage is limited to34%, which is approximately one-third of all theresidents. Furthermore, the evacuation percentage
increases dramatically to approximately 70% whenevacuation instructions are issued. We can also seethat at the final point when the residents’ homes are
beginning to flood, the percentage of residents who
have determined to evacuate reaches about 92%,
meaning that approximately 8% of the people haveno intention to evacuate their own homes at any of
the stages.
In determining the intent to evacuate when subjected to persuasion at each stage prior to the issuingof the evacuation instructions, the percentage of residents who have determined to evacuate is about
20% higher than when they are not subjected toevacuation persuasion. However, after the evacuation instructions are issued, that difference decreases.
These results show the importance of persuasion topromote evacuation in the initial stages of a disaster.
The timing of the occurrences in the simulation ofeach event corresponding to the conditions of Table
3 is shown in Fig.5. To establish the timing of these
events, we used the time difference between theevacuation advisory and the embankment failure
obtained as-is from the calculation results of theabovementioned river model. Using the time difference, the timings when the flood warning and theriver-related information would be transmitted wereCalculation Warning Evacuation River In- Evacuationare Started Information Advisory formation Instructions(No.1,2)0(No.3)

1(No.4)

2(No.5)

3

Dike
Failure(No.6)

4

Time Require for
Evacuation Preparations(No.7)

5

6

N=3,000

70 minor more10%60-70 min20%

7(hour)

Evacuation Destination

set using a time of two hours prior to the issuance ofthe evacuation advisory as the calculation start time.
For this situation, conditions that had already occurred prior to starting the simulation were assumed
as conditions No. 1 and No. 2. In addition, conditionNos. 8 and 9 occurred at different timings for eachindividual depending on the location and flooding
status of their homes. The timings shown in Fig.5
actually show the start of information transmission
using the occurrence of an event as the trigger, andtherefore, the exact timing of the occurrence of anevent is the timing at which each resident receives
information via transmission by radio communications for disaster prevention, mass media, etc.
Therefore, if a resident is unable to obtain information, the relevant event does not occur.
In the evacuation behavior model, the determination of intent to evacuate is decided by residents
according to the conditions for receiving disaster
information and evacuation persuasion. More specifically, if the information that was obtained was
information that indicated a situation in which theresident responded that he or she would decide toevacuate, or was a later situation, the resident was
treated as having made the decision to evacuate.
Furthermore, if information was obtained from another resident who had already decided to evacuate,
this was viewed as having been subjected to evacuation persuasion, and depending on the evacuation
intention at the time that the resident was subjected
to persuasion, the resident either decided at thatpoint in time to evacuate or would decide to evacuate, depending on the details of the information thatwas received.
c) Timing at which evacuation started
Responses pertaining to the time required for
preparations from the moment the decision wasmade to evacuate until the resident left his or herhome to evacuate are shown in the top portion of
Fig.6. According to these results, the number ofhouseholds that can complete preparations within 30
minutes from the time that the decision was made toevacuate was limited to 36%. Moreover, the response that accounted for the largest percentage of
responses indicated that a preparation time of 30–40
minutes would be required, and this was the case for
one-third of all respondents. On the other hand, asimilar number of respondents indicated that theywould need one hour or longer to prepare. In theevacuation behavior model, the timing at which

Location outside of
Edogawa Ward 20%

Evacuation Method

Other
2%

Area of higher
elevation 8%
Home of a
relative or
acquaintance2%
Private facility
1%10-20 min21%

20-30 min9%30-40 min30%

50-60 min 1%
40-50 min 4%

Fig.5 Event occurrence timing.

Less than 10 min 6%

Publicfacility
16%

Nearbyelementaryor middle
school51%

N=3,000

Automobile
15%

Other 3%
Bicycle 4%
Taxi 3%Motorcycle 1%
Ride in vehicle ofAnother home 1%
Walk73%
N=3,000

Fig.6 Time required for evacuation preparations and behavior at time of intention to evacuate.

evacuation was started was determined by adding
the time required to prepare for evacuations once thedecision was made to evacuate.
d) Evacuation behavior
The tallied results for the survey items relating tobehavior at the time of evacuation are shown at thebottom of Fig.6.
First, the ward’s evacuation plan includes evacuation to both regional disaster prevention centers(higher elevation) as a basic evacuation destinationand shelters (elementary and middle schools) as
emergency evacuation destinations; however, inconsistencies occurred between the ward’s evacuation
plan and the evacuation destinations desired by theresidents. More specifically, many respondents replied that they would evacuate to a nearby elementary or middle school, and when other public facilities were included, the percentage of respondents
who intended to evacuate to a facility of the ward
reached 67%. On the other hand, respondents who
expected to evacuate to a higher elevation location
was limited to 8%, which was a very limited percentage. Twenty percent of the respondents imagined broad-based evacuation to an area outside theward.
Next, when asked about their evacuation methods,
73% of the respondents assumed that evacuation
would be done by walking, which was characteristicof the group. Evacuation using an automobile also
included evacuating as a passenger in someone
else’s car or using a taxi or motorcycle. The percentage of respondents falling into these categories
was around 20%. When considered in terms of absolute numbers, it is clear that the number of automobiles used for evacuation would be large.
In the evacuation behavior model, the evacuation
destination and method for residents who have started evacuation behavior were selected based on these
responses. To simplify the model, if evacuation was
done to a public facility, a private facility, or a home217

Presence of family member who
would have difficulty evacuatingon his or her own power.

I don’tknow
13%
Not Present
90%

N=3,000

Table 5 Strategy scenarios.

Availability of evacuation support
by family, etc. for persons having
difficulty evacuating.

Present
10%

Evacuation would
be difficult incurrent state 9%

SymbolStrategy Scenario

Modeling Method

A1

Support households who would
have difficultyevacuating.

Implement strategies to support
households that would have difficultyin evacuation and enable evacuations
based on the condition of requiring
double preparation time.

A2

Take measures toensure that allhouseholds receive information.

Improve information transmission sothat all residents receive the information within one hour from the timethat information transmission started.

A3

Improve evacuation timing.

Improve the awareness of residents sothat all residents decide to evacuate atleast by the time they hear the evacuation advisory.

A4

Improve evacuation preparation
time.

Assume advance preparations during
normal conditions so that evacuation
can be started within 30 minutes fromthe moment the decision is made toevacuate.

B1

Prohibit dangerous evacuations
by residents living
in upper stories.

After information of a dike break has
been obtained, prohibit the evacuationof residents living on the third floor or
higher.

B2

Limit the numberof people targeted
for evacuation.

After the information of an evacuation
advisory has been obtained, prohibit
the evacuation of residents living on
the third floor or higher. Evacuate allresidents living on the second floor or
lower by the time the evacuation
advisory has been issued.

C

Guide evacuees toregional disasterprevention centers.

Based on evacuation to regional disaster-prevention centers, do not evacuate to shelters other than in an emergency after flooding begins.

D1

Introduce roads
for pedestrians.

After an evacuation advisory has been
issued, partition a part of the roads foruse by pedestrians (see Fig.11).

D2

Decentralize
evacuation destinations.

Allow only those households withvulnerable family members to evacuate to shelters.

D3

Promote evacuation in advance.

Complete the evacuation of households with vulnerable family members
two hours prior to the evacuation
advisory.

Can evacuate
because of
support fromnon-family
members 4%

Can evacuate withfamily support
74%
N=312

Fig.7 Presence of family members who would have difficultyevacuating on their own and availability of evacuation
support by family members, etc.

of a relative or acquaintance within the ward, a condition of evacuation to a shelter was assumed. In
addition, if a resident indicated that he or she would
evacuate to an area outside the ward, such a casewas modeled using an evacuation to virtual facilities
distributed at the end points of the network of roads
that led to the outside of the ward. Furthermore, theevacuation methods were selected as either walking
or by car. If the response indicated that evacuation
would occur by walking or by bicycle, the condition
was modeled as evacuation by walking, and for allother cases, the condition was modeled as evacuation by car.
e) Persons experiencing difficulty in evacuatingon their own
The two graphs shown in Fig.7 are respective responses to questions regarding (1) the presence of afamily member who would have difficulty evacuating on his or her own power and (2) whether or notevacuation support could be provided for households having family members who would have difficulty evacuating on their own. When we look atthese results collectively, it is clear that 10% of allhouseholds have family members who would havedifficulty evacuating on their own, and at least about
10% of such households would have difficulty inobtaining support for evacuation by other family
members. Therefore, evacuation on a household unit
would be difficult.
These results were reflected in the evacuation behavior model, and a condition that included thepresence of a person who would have difficultyevacuating under his or her own power and forwhich evacuation would be difficult was modeled asa state in which evacuation to a location outside of
their own home was not possible. Half of the responses of “I don’t know” regarding the availabilityof support by family members and others for persons who would have difficulty evacuating werehandled as not being capable of evacuating.

5. SCENARIO ANALYSIS
To analyze scenarios for examining measures toreduce damage in the event of large-scale flooding
in a large city, we first implemented simulations ofscenarios that reproduced the intended behaviors of
the residents, as understood from the survey (hereinafter, the current status reproduction scenario).
Using that scenario, we gained an understanding of
the assumed-damage scale with the current conditions and the factors thereof. Next, we implemented
simulations of various scenarios that modeled damage-reduction strategies based on the factors thatgenerated the damage (see Table 5), and we thenverified the results and identified new issues.

218Requiring
emergency rescue
37,158 people, 6%Completed evacuation
435,501 people67%
Not yet decideto evacuate
133,148 people
20%

In process ofevacuation
13,239 people Preparing for2%
evacuation
32,687 people
5%

Preparingforevacuation
433 people,
Composition of Residents
58%
Requiring Emergency
Rescue Prior to Starting
Evacuation

N=37,158

In process ofevacuation
36,417 people, 98%
Prior to starting
evacuation
741 people, 2%People staying
within a flooded area

People in danger
within a flooded area

7

People requiring
emergency rescue
63.5

6
48.5

49.4

Number of people
starting evacuation
65.2 70
60

53.7

49.3
50

5

40

4
6.3

3
2

4.5
3.7

4.1

3.9

3.5

3.8

4.3

0.6

0.5

0.7

30
20

2.4

1
0

4.1

10
0.60.80.6

0.0 0.80Implement
Improve
Current
Support Measures to Improve
Statushouseholds ensure all evacuation evacuation all strategies.
Reproduction who have households timing (A3). preparation (A1-A4)
time (A4).
receivedifficultyevacuating information
(A1).
(A2).

Residents who havedifficulty evacuating
90 people, 12%
Did not receive
advisory
71 people, 10%Prior to making
decision to evacuate
147 people, 20%

Number of People Who Have
Started Evacuation (10,000 people)

N=651,733

Composition of Residents
Requiring Emergency Rescue

Flooded Population (10,000 people)

Composition of All Residents

Fig.9 Flooded population and change in the number of people
starting evacuations according to strategy scenarios.

case in which each scenario for each strategy was
conducted independently with respect to the current
status reproduction scenario and for a case in which
all the strategies were implemented collectively. The
definition of each flooded population, such as thenumber of people who stayed within the floodedarea, is shown in the diagram in accordance with
Table 1. From these results, if we look at the change
in the number of evacuees, when each strategy isimplemented independently, the evacuation percentage and the timing at which evacuation is started are
improved, and as a result, more residents begin
evacuating compared to the current status reproduction scenario. When all the strategies are implemented collectively, it is clear that approximately
650,000 people, a number that corresponds to theentire population, begin to evacuate. However, when
we focus on the flooded population, with the scenarios other than A2, it is clear that the introduction of
improvement measures has the opposite effect andresults in an increase in the number of people requiring emergency rescue. In particular, with the current
status reproduction scenario, 45,000 residents stayedin the flooded region; however, when all of thestrategies were implemented, the number reduced tozero. However, people requiring emergency rescue
due to serious injuries totaled 63,000, approximately
1.7 times more than the level estimated with the current status reproduction. This result suggests that thepromotion of stereotypical evacuation behavior of
all residents with current conditions has the risk ofleading to an increase in injury. We also analyzed
the individual implementation of scenarios for eachstrategy, and found that while scenario A3 involved
active evacuation at the evacuation advisory stage,
when the evacuation scale became very large, theevacuation facilities became overcrowded, congestion became a serious concern, and the amount of
injuries increased. In addition, scenarios A1 and A4
did not involve improvements in the timing at which
the decision to evacuate was made, and therefore,
the number of people evacuating under dangerous
conditions after the evacuation advisory increased,

N=741

Fig.8 Results of simulation of current status reproduction.

(1) Assumed damage with current conditions
based on the behavioral intentions of the residents
Figure 8 summarizes the conditions of each resident when 24 hours had passed since the beginningof the current status reproduction scenario. The
number of residents requiring emergency rescue
would exceed 37,000, and it is clear that large scale
injury would result. It is also clear that 98% would
be injured while evacuating and would require
emergency rescue. Regarding the contributing factors, many residents decided to evacuate when therisk of flooding had increased after the evacuation
advisory (see Fig.4). Thus, it is clear that these residents became engulfed in the flooding while they
were evacuating. Further, if we look at the composition of the residents requiring emergency rescue
prior to beginning evacuation, approximately 60%
are in the middle of evacuation preparations, 20%
do not have any intention to evacuate outside theirown homes, and the rest are either residents who
have not been able to receive any evacuation information or residents who would have a difficult time
evacuating. From these results, with the current status reproduction scenario, it is clear that injury iscaused by various factors, including a delay in thedetermination of intent to evacuate. It is important
to note that many shelters ultimately reached anover-capacity state.
(2) Effects of improving evacuation behavior
Based on the problems identified from the current
status reproduction scenario, we determined the injury reduction effects from improving the evacuation percentage and the timing at which evacuation
began. The assumed strategies are the scenarios A1–
A4, shown in Table 5.
Figure 9 summarizes the number of people whostarted evacuating and the flooded population for a219

resulting in an increase in injuries. On the other
hand, by ensuring that information was thoroughly
conveyed with scenario A2, the evacuation of residents who made the decision to evacuate at an earlystage was reliably started, and the evacuation timing
became faster. In addition, because evacuation intentions were based on an awareness of the current
state, a significant increase in the number of evacuees, as seen with scenario A3, did not occur, and as
a result, the amount of injuries decreased.
From the above analysis, we were able to confirm
the following problems that occur in conjunction
with an increase in the number of evacuees.
Problem of risky evacuation behavior: If residents who live in high-rise buildings stay in theirown homes, and if their homes become flooded,
they require emergency rescue when they evacuate
their homes.
Problem of the capacity of evacuation facilities: The capacity of shelters is expected to be exceeded even with the current status reproduction
scenario, and is reached at an even earlier timing
when the number of evacuees increases.
Problem of evacuee congestion: The increase in
the number of evacuees causes serious vehicle andpedestrian congestion, resulting in a slowdown of
the evacuation time.
Significantly, assuming a condition in which noneof the residents evacuate, the number of people requiring emergency rescue decreases to approximately 6,000 people, which is significantly lower thanthe current status reproduction scenario. However,the number of people who becomes surrounded byflood waters increases to 310,000, and therefore,
some response is required.

*Implemented every strategy of A1, A2 and A4.

65.2

7

56.4

53.6

6.3

56.4

60

6

50

5

4.3

4.0

4

4.3

40

3.6

3.2

2.9

3

30
20

2
0.8

1
0

70

0.0

0.4

0.8

0.7

If all residents Prohibit dangerous Limit the number Guide to regional
evacuations by of people targeted
disasterhave evacuatedresidents living infor evacuation prevention centers(Reference)
upper stories (B1)
(B2)
(B2+C)

10

Number of People Who Have
Started Evacuation (10,000 people)

Flooded Population (10,000 people)

People staying
People in danger
People requiring
Number of people
within a flooded area within a flooded area emergency rescue starting evacuation

80

Fig.10 Effects of limiting evacuees and guiding them toregional disaster-prevention centers.

Fig.10). However, when compared to the current
status reproduction scenario, the reduction in thenumber of people requiring emergency rescue isminimal, and it is clear that a large amount of injury
still occurs. Moreover, limiting the number of evacuees resulted in 40,000 people being surrounded byflood waters, and therefore, separate measures thattarget these residents are required.
(4) Injury reduction effect by improving evacuation destinations
While 70% of the residents intend to evacuate toa public facility within the ward, the total capacityof shelters is approximately 270,000 people. Therefore, if a large-scale evacuation is implemented, itwould be unrealistic for residents to evacuate as intended. On the other hand, regional disasterprevention centers have enough capacity to accommodate a total of over 2 million people. In theward’s evacuation plan, these centers are stipulated
as priority locations for the evacuation of residents.
In the next analysis, we established a scenario (C)that used these regional disaster-prevention centers.
In this scenario, regional disaster-prevention centerswere stipulated as the evacuation destination according to the residential district. However, when information of a collapse of the embankment or floodingof the surrounding region was obtained, evacuation
to the nearest evacuation location that included shelters was assumed as the emergency evacuation behavior.
If we examine the results of adding the conditionsof C to those of B2 (right side of Fig.10), we canconfirm that injury is reduced by stipulating the appropriate evacuation destination. However, since thedecrease is approximately 4,000 people, the numberof people requiring rescue still remains over 30,000.

(3) Injury reduction effects from evacuation demand reduction measures
Based on the results described in the previous
section, we conducted simulations that assumed
strategies to reduce risky evacuation behavior immediately before flooding and after flooding. Morespecifically, the simulations included prohibiting
evacuation by residents living on the third floor or
higher after a dike break (B1), evacuating all residents living on the second floor or lower by the timethe evacuation advisory was issued, and prohibiting
the evacuation of residents living on the third flooror higher once an evacuation advisory was issued(B2). These scenarios also assumed that the strategies for A1, A2, and A4 had already been implemented.
If we examine the simulation results for B1 andB2 in Fig.10, it is clear that both scenarios significantly reduce injury when compared to the scenario
in which all residents were evacuated (left side of(5) Injury reduction effect from dispersing evacuees
While a reduction in the number of injuries wasobserved in improving the evacuation destination, a220

0 - 1,970
1,971 - 7,587
7,588 - 19,008
19,009 - 46,539
46,540 - 104,731

People in danger
within a flooded area
4.3

People requiring
emergency rescue

Time required toevacuate
140*Implemented every strategy of A1, A2, A4, B2 and C.

120

4.3

4
3.2

3.2

82.8

3

97.4

64.4

80
60

2
1.2
1
0

100

0.9

0.7
0.0 0.1

Without
Dispersion(Reference)

Introductionof roads forpedestrians
(D1)

0.2

0.1

Decentralization Promotion ofAdvanceof Evacuation
Evacuation
Destinations
(D3)
(D2)

40
20
0

Average Time required to evacuate(minutes)

Flooded Population (10,000 people)

Number of Passing
Pedestrians (people)

People staying
within a flooded area
5
121.7
4.3

Fig.12 Effects of dispersing evacuees.

Regional Disaster Prevention Center
Pedestrian Exclusive Road

Fig.11 Number of pedestrian evacuees passing on roads andareas where pedestrian-exclusive roads are introduced.

nearby shelters, the evacuation time was reduced byanother 20 minutes compared to D1. However, congestion by evacuees heading to regional disasterprevention centers was not adequately alleviated,
and the injury reduction level was limited to approximately 20,000 people. Finally, with strategy D3,
the evacuation of vulnerable households was completed at an early stage. Therefore, congestion at thetime of the evacuation advisory was alleviated, andthe number of people requiring emergency rescue
was reduced to approximately 9,000 people, withfewer experiencing injury compared to D2. However, the evacuation time of ordinary households heading to regional disaster-prevention centers was not
significantly improved. When the strategies of D1 to
D3 were implemented together, the number of people requiring emergency rescue was reduced to zero.

large number of evacuees were guided to a small
number of regional disaster-prevention centers.
Consequently, large-scale congestion occurred alongsome roads particularly due to pedestrian evacuees(see Fig.11). Congestion occurred because of evacuees converging on specific roads in numbers that
temporarily exceeded capacity. Accordingly, toeliminate the congestion, a strategy that spatiallyand temporarily disperses evacuees is conceivable.
To develop spatial dispersion strategies, first a scenario was developed in which some of the roads
were designated exclusively for pedestrian use afteran evacuation advisory was issued (D1, see Fig.11
for the targeted roads), and a strategy was established in which the evacuation destinations weredecentralized by selecting evacuation destinations,
including shelters for vulnerable households(households with preschool-age children or elderlyfamily members, approximately 24% of all households) and households with family members who
would have difficulty in evacuating (D2), were established. With D1, it was assumed that the roads
designated exclusively for pedestrian use were wellknown, and in the search for pedestrian courses, theroads were evaluated at half the normal cost. Next,
as a temporal dispersion strategy, a case in which
evacuation of vulnerable households was completed
two hours prior to the evacuation advisory (D3) wasestablished with the assumption that the prior evacuation of vulnerable households was promoted.
The flooded population and the average evacuation time of pedestrian evacuees for conditions D1
to D3 based on strategies B2 and C is summarized
in Fig.12. From the figure, if we examine the results
for strategy D1, it is clear that the evacuation time
was reduced by approximately 40 minutes compared
to strategy C, which required approximately 2 hours
for evacuation; congestion was also alleviated. Further, a total of approximately 30,000 people requiring emergency rescue was significantly reduced toapproximately 1,000 people. Next, with strategy D2,
because vulnerable households could evacuate to(6) Summary of scenario analysis results
In this section, we summarize the issues andstrategies for reducing the number of flooding victims in large cities, as identified from the scenario
analyses implemented in this study.
Improving the awareness of residents: The
awareness among residents regarding flooding disasters is low, and with the current awareness levels,
many residents would engage in risky behavior in
the event of a flooding disaster, such as evacuatingwhen their own homes are flooded. To reduce thenumber of victims, civil authorities must understandthe flooding risks of cities and improve awareness topromote appropriate responses. Therefore, we mustalso promote disaster prevention education during
normal periods.
Reducing evacuation demand: Large-scaleevacuations of cities result in problems such as congestion of evacuees and evacuation facilities exceeding their capacities. Therefore, these evacuations also carry the risk of increasing the number ofvictims. Accordingly, measures to reduce the demand for evacuation are required, such as recommending that residents of high-rise buildings wait intheir own homes. However, in zero-meter areas,
flood waters could remain for an extended period of221

time, and therefore, secondary evacuation plans foruse after human life has been secured must also be
examined and implemented. This includes plans topromote reserve supplies in each home and rescueplans in the event of a flood.
Dispersion of evacuees: If evacuation is initiatedonce an evacuation advisory is issued, the concern isthat congestion of evacuees could become serious.
Therefore, an intrinsic issue is advancing with thetemporal dispersion of evacuees by promoting proactive evacuation from an early stage. Achieving
this requires that information be aggressively provided from an early stage using the latest technology
for weather forecasts and water-level predictions.
Appropriate evacuation destinations must also be
specified, and strategies to alleviate congestion must
be implemented to spatially disperse evacuees. The
most serious concern arises when large numbers ofevacuees temporarily converge in small-scale evacuation facilities. Therefore, evacuation facilities thatcan meet the evacuation demands of each region
must be established. However, the establishment ofa small number of large-scale evacuation facilities
comes with the risk of congestion becoming a serious problem, and therefore, caution must be exercised. Furthermore, if the establishment of evacuation facilities is difficult, extensive evacuation that
includes evacuation to higher elevations or areas
outside the region must also be examined, which
would require coordination with surrounding regions and measures to educate the residents. The
most effective measure for cities in low-lying areas
is to realize early-stage evacuation to areas outside
the flood zone by promoting both types of dispersion measures.
Transmitting information to all residents, andsupporting residents requiring relief in a disaster: To eliminate human suffering, it is essential tosupport residents who do not receive evacuation
information or would require relief assistance during
a disaster. It is particularly difficult for large cities
to ensure that information is transmitted to every
resident due to various problems such as the presence of a large, diverse population, and the lack ofcohesivenessincommunities.
Conceivablemeasures to resolve this problem include diversifying the media sources used to transmit informationand fostering independent efforts to obtain disasterrelated information. Moreover, support for residents
requiring evacuation assistance is a basic problem,
which does not depend on regional characteristics.
In cities with large populations, it is difficult for thegovernment to be the sole source of support during
disasters, and therefore, there is a demand to develop support organizations by engaging residents in
the region.

6. CONCLUSION
In this study, our objective was to examine appropriate evacuation strategies for large-scale flood
damage in a large city. To achieve this, we developed a simulator to model the spatial characteristicsof the region, including its topography and evacuation facilities, as well as the current status of theresidents of an area. This simulator was used to specifically model large-scale evacuations based on the
behavioral intentions of the residents using an entire
large city as the target. In addition, the simulator
was also capable of modeling conditions, such as
the disaster phenomenon, the transmission of information, and the damage in the event of large-scale
flooding; thus, we were able to use it as a tool to
examine evacuation measures in a large city. However, some issues remain that must be resolved toachieve a more realistic examination, such as considering the impact of inland flooding and flow
speed on the evacuation of residents.
In this study, we used the developed simulator to
conduct scenario analyses of the Edogawa Ward.
From the analyses, we could identify the evacuation
problems that are inherent in large cities, and foundthat the promotion of stereotypical evacuation behaviors temporarily creates a large number of evacuees, compounds the problems of congestion, andcauses evacuation facilities to exceed capacity, resulting in an increase in injuries. We also confirmed
that, to reduce injury in the event of a flooding disaster in a large city, it is important that a wide rangeof evacuation strategies be developed to improve theawareness of the residents, reduce the evacuation
demand, and temporally and spatially disperse
evacuees.
The results of this study are limited to indicating
the framework for evacuation strategies in cities,
and therefore, to devise practical evacuation plans
for a specific region, examinations based on actual
conditions of a region must be implemented. Furthermore, the appropriate implementation of anevacuation plan requires the understanding of theresidents in the region to be evacuated, and therefore,
activities to raise awareness are extremely important.
ACKNOWLEDGEMENTS: We thank the cooperation of the Edogawa Ward in the implementation ofthis study. Mr. Kyohei Hosoi of IDA Co., Ltd., cooperated in the construction of the river model andfloodplain model, for which we are grateful.
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2)(Received April 17, 2015)

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