Proceedings of Coastal Engineering, JSCE, Vol.3, 2012, November

EVALUATION OF TSUNAMI EVACUATIONBUILDING DEMAND THROUGH THE
MULTI-AGENT SYSTEM SIMULATION OF
RESIDENTS’ BEHAVIOR
Erick MAS1, Bruno ADRIANO2, Shunichi KOSHIMURA3, Fumihiko IMAMURA1,Julio KUROIWA H.4, Fumio YAMAZAKI5, Carlos ZAVALA6, Miguel ESTRADA6
1International Research Institute of Disaster Science, Tsunami Engineering Laboratory. Tohoku University, Japan,erick@tsunami2.civil.tohoku.ac.jp; imamura@irides.tohoku.ac.jp
2Tohoku University, badrianoo@geoinfo.civil.tohoku.ac.jp
3 International Research Institute of Disaster Science, Laboratory of Remote Sensing and Geoinformatics for Disaster
Management. Tohoku University, Japan, koshimura@irides.tohoku.ac.jp
4 Emeritus Professor, Faculty of Civil Engineering, National University of Engineering. Peru,
jkuroiwah@infonegocio.net.pe
5Department of Urban Environment Systems, Graduate School of Engineering, Chiba University, Japan,
fumio.yamazaki@faculty.chiba-u.jp
6Japan Peru Center for Earthquake Engineering Research and Disaster Mitigation - CISMID, Faculty of Civil
Engineering, National University of Engineering, Peru, czavala@amauta.rcp.net.pe; estrada@uni.edu.pe

In plain areas prone to tsunami, finding a way to shelter and escape from the inundation becomes adifficult task for residents. The 2011 Great East Japan Tsunami has shown that the horizontal evacuation
using cars can compromise the safety of people. Another alternative is the vertical evacuation. In manycases, not only the capacity of these shelters plays an important role, but the spatial distribution and theevacuee preference for the nearest shelter. Such preference and location creates a conflict between
capacity offer and demand. In this paper, we used an agent based model to simulate the evacuation ofpedestrians and cars in La Punta, Peru. Twenty designated buildings for vertical evacuation are available
for sheltering and escape from tsunami. The stochastic simulation of population spatial distribution andtheir refuge preferences revealed the over demand of some shelters. Finally, a capacity-demand map was
created to share results with local authorities as a first step for future countermeasures in the district.
Key Words: tsunami evacuation, building demand, agent based simulation, human behavior

1. INTRODUCTION
Finding shelter during an emergency of tsunami
for plain areas with a fast arrival time of waves is achallenging task for local people. The verticalevacuation to high buildings rising over theexpected inundation depth in the area is one of themost suitable alternatives for this kind of urban
districts. However, if the spatial distribution
combined with the available capacity of thesestructures is not well displayed, conditions ofover-demand and under-demand will be observed
among them. In this paper, we conducted thenumerical simulation of a future tsunami scenario
using a predicted slip distribution model provided

by Pulido et.al. (2011) from the interseismic
coupling model estimated by Chlieh et.al (2011).
Tsunami inundation is integrated with the agent
based model of human evacuation. Using thestochastic simulation of several possible scenarios,
the relation capacity-demand at each official
Tsunami Evacuation Building in La Punta district in
Peru was evaluated. The Capacity-Demand Index(CDI) is introduced as a way for mapping andidentifying areas for future mitigation actions bylocal stakeholders.

2. THE STUDY AREA
(1) Historical Tsunamis near La Punta- 61 -

La Punta is part of the Constitutional Provinceof Callao in central Peru and one of the six districtsin the First National Port city of Callao. It is apeninsula area and one of the smallest districts inPeru, with 4,370 inhabitants (INEI, 2007) and a total
land area of 0.75 km2 (Fig. 1).

with a future large earthquake and tsunami.

Fig.2 Tsunami Evacuation Buildings (TEBs) in La Punta,Callao – Peru. The map shows the spatial distribution of TEBs
and “C” is the capacity reported by the municipality.

Fig.1 Overview of the study area. Location.

Major risk of earthquakes and tsunamis arepresent in this area, due to its low topography. La
Punta was affected by several historical earthquakes
and tsunamis, such as the July 9th, 1586, earthquakeof magnitude 8.6 and a local tsunami height ofaround 5 m (Dorbath, 1990), although some reports
give a much larger value of 24 m (Berninghausen,1962). Another two earthquakes on October 20thand 21st, 1687, of magnitudes 8.0 and 8.4,
respectively, struck this area. The first one had
generated a 5 m to 10 m local tsunami, while thesecond might have been located in southern areas(Dorbath, 1990). One of the most memorable
earthquakes in the Callao region is the greatearthquake of October 28th, 1746, with a magnitude
8.0 to 8.6 which completely destroyed some central
Peruvian coastal cities. A tsunami of 15 m to 20 m
in height resulted from this earthquake, it wasreported half an hour after the ground shaking, andwashed away Callao city with a 24 m run-up, killing
90% of its population (Kuroiwa, 2004). Twocenturies later, the Peruvian central coast
experienced more activity on May 24th, 1940, witha local earthquake and tsunami of 3 m in height. TheOctober 3rd, 1974, event in front of Lima had amagnitude of 8.0 and a local tsunami height of 1.6m (Langer and Spence, 1995). Since then, no large
seismic activity has been reported in front of Callao
area. A possible seismic gap might be located in thisarea, threatening La Punta and other coastal cities(2) Tsunami Evacuation Buildings
There are 19 official evacuation buildings inthe district and a 20th building is locatedimmediately outside of the district in the Callao
province (Fig. 2). Previously it was considered in
the evacuation plan, however after the 2007 Pisco
Earthquake it suffered some structural damages andrequired retrofitting. Since then the district lost
confidence on this building; however in 2009 it wassuccessfully retrofitted.

3. TSUNAMI NUMERICAL SIMULATION(1) Tsunami source
An instantaneous displacement of the sea
surface identical to the vertical sea floor
displacement is assumed in the model of tsunami
source. The source of tsunami simulation is a resultof the slip deficit rate with the interseismic period of
265 years since the 1746 historical earthquake inPeru (Fig. 3).
(2) Numerical Modeling
The Tohoku University’s Numerical Analysis
Model for Investigation of Near-field tsunamis(TUNAMI model) was used as the tool of tsunami
modeling (Imamura, 1995). A set of non-linear
shallow water equations are discretized by theStaggered Leap-frog finite difference scheme.
Bottom friction condition is in the form ofManning’s formula constant in the whole domain.

- 62 -Table 1 Agent type number at each scenario and theirmaximum speed value allowed during simulation

TypeVe rtic al

KIDS
514
TEENS
377
ADULTS
2428
ELDERS
1051
CARS(*) Units: persons

Fig.3 Slip distribution from GPS data. There are 280 sub-faultsof 20x20km. (Pulido et.al, 2011) (Figure in Yauri, S. (2011)).

4. EVACUATION SIMULATION
We used the TUNAMI-EVAC1 (Mas et al., 2012a)
to observe the tsunami inundation together with theresident evacuation behavior. The model wasdeveloped in NetLogo, a multi-agent programming
language and modeling environment for simulatingcomplex phenomena (Wilensky, 2001). The model
uses GIS data as spatial input and the tsunami
numerical results to evaluate the probability ofcasualty at each step. Casualty estimation is a binary
logistic model based on the experimental results ofhuman instability reported in Takahashi et.al (1992).
The population from the census data wascategorized in four groups according to the age (Fig.
4).

Ho rizo ntal& Ve rtical
514
377
1678
901
225

Max.
Spee d(m/ s)
1.06
1.33
1.33
0.93
8.40

(1) Cases for Simulation
For a better comprehension of the necessity ofTsunami Evacuation Buildings, a case for the only
horizontal evacuation with no use of TEBs wasconducted. Next, a Vertical Evacuation case and a
combined case of Horizontal Evacuation andVertical Evacuation were evaluated. These are theconditions for each case:a) Horizontal Evacuation
Pedestrians and cars choose one of the possible
two exits out of the district – two streets leading to
the northeast of the district (Fig. 6 - triangles).

Fig.6 Location of TEBs (circles) and Exits (triangles)

Fig.4 Age distribution in La Punta and four groups of agent
type for simulation.

The main difference in all these groups or agent type
is the maximum possible speed during evacuation.
The speed varies as a half tail normal distribution ofdensity in the agent field of view of 60 degrees conewith 5m distance for pedestrians and 10m for cars.
Table 1 shows agent types and their maximum
allowed speed.

b) Vertical Evacuation
Twenty available TEBs are used for sheltering(Fig. 6 - circles). Evacuees – only pedestrians –choose the nearest shelter to their location regardlessthe capacity or condition at the shelter. To observe
the over-demand of shelters, evacuees are allowed
to enter the building without any restriction.
Vehicles are not included in this case.
c) Horizontal and Vertical Evacuation
This is a combination of the two previous
scenarios. Here, pedestrians and cars evacuate to
any of all TEBs or Exits available in Fig. 6.
(2) Start time of evacuation
In all cases the start time condition ofpedestrians and cars follow the Tsunami Departure
Curve method proposed in Mas et al. (2012b)(Fig.7).

- 63 -Fig.7 Tsunami Departure Curves for the simulation cases.
Behavior of start time is obtained through the stochastic
simulation of random selected times bounded by two Rayleighdistributions. Left distribution obtained through statedpreference surveys and the other distribution is based on theestimated arrival time of tsunami from numerical simulation.

5. RESULTS & DISCUSSION(1) Casualty Estimation
Due to the many possibilities of spatial initialcondition of residents, in the model, outcomes areaveraged from a set of repetitions (250 runs) ofdifferent scenarios with agents loaded at random
locations in the district (indoors or outdoors). For
the result of these scenarios, it was observed that theVertical case yields to fewer casualties (16 pers.)
due to the short distance for a rapid evacuation andthe less use of vehicles that contributed to stagnation
points in the other two cases. The Horizontal Caseshows the maximum average number of casualties(271 pers.), followed by the Horizontal and Vertical
Case (153 pers.). As expected traffic congestion isobserved along the exit roads and in particular nearto the exits until the arrival of tsunami. Snapshots at
0, 10, 20, 30, 35, 40min of the first case ofsimulation are shown in Fig. 8, while Fig. 9 shows
the 40min snapshots of the other two cases.
(2) Shelter Demand
The number of agents sheltered at each
TEB was counted and averaged from all repetitions;a comparison of demand and capacity is shown inFig. 10. Out of twenty (20) TEBs, 13 resulted withover-demands, while seven (7) were still withavailable space. It is necessary to ensure the safetyof evacuees even at the over-demand shelters. As anexample, building S11 with 25 persons of capacity
may expect around 180 residents in the area looking
for shelter. New areas for vertical evacuation shouldbe implemented to support the rapid evacuation ofresidents in the area.

Fig.8 Horizontal Evacuation Case. Casualties are observed attraffic congestion near the exit of the district. The necessity of avertical evacuation is revealed here.

Fig.9 Snapshot at 40min simulation for the Vertical Evacuationcase (left) and the Horizontal and Vertical Evacuation case(right). White dots show areas of stagnation.

Fig.10 Capacity and Demand of Shelters in La Punta. Out of 20TEBs, 13 might present over-demand and 7 under-demands.

(3) Mapping the Capacity-DemandIn order to facilitate the local authorities’
decision of future countermeasures and alternatives
for evacuation in La Punta, the resulting- 64 -

capacity-demand rate is mapped as observed in Fig.
11.

and their location according to the nearest shelter
selecting behavior of evacuees. A CDI Map is
proposed as a first step for future mitigation plans inthe district. It is recommendable to allocate newareas for evacuation near to the north area of thedistrict (close to the beach).
ACKNOWLEDGMENT: We would like to express
our deep appreciation to the Ministry of Education,Culture, Sports, Science and Technology (MEXT) andJST-JICA (Peru) for the financial support throughout
the study. Also, a special thanks to the authorities
and residents in La Punta for their participation andinsights.
REFERENCES

Fig.11 Capacity-Demand mapping of TEBs. Black (overdemand) - White (under demand) (H&V case).

The relation capacity vs. average demand of cases
represents the Capacity-Demand Index (CDI)
following the simple relation in Eq. (1)
CDI=DemandCapacity(1)

Therefore, a value of 0.0 to 0.5 indicates that lessthan a half of the structure has been occupied; from0.5 to 1.0 at most the total capacity of the structure
is being used. The important information comes for
CDI values over 1.0, where over-demand conditions
are probable. As a result, from the CDI Map it ispossible to conclude that unfortunately the nearest
buildings are comparatively of lower capacity withthe ones located at the head and neck of the district.
Due to the behavior of shelter selection (nearest),
most of the structures closer to the beach area in thenorth coast present an over-demand while the south
population apparently may fit in the availablestructures.

8. CONCLUSION
An evaluation of Tsunami Evacuation Building
demand has been conducted through the simulationof agents in evacuation in La Punta. In this paper wehave shown that the horizontal evacuation mightlead to a high number of casualties due to the traffic
congestion at the neck of the district. As estimated,the vertical evacuation would be a suitable solution
for this area; however the spatial location of shelters
complicates the evacuation process. Here, we
identified the shelters with possible over-demand

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(Received June 15, 2012)- 65 -