Journal of Natural Disaster Science, Volume 38，Number 2，2017，pp179－200

Information Exchange Model for Multi-Agent Earthquake/Tsunami
Evacuation Simulation
Claus Aranha*, Hiromu Matsushima**, Hitoshi Kanoh**Faculty of Engineering, Information and Systems, University of Tsukuba**Graduate School of Science and Information Engineering, University of Tsukuba(Received: Mar. 15, 2017

Accepted: Dec. 18, 2017)

ABSTRACT
We propose an agent-based simulation system for evacuations after earthquakes and tsunami. We focus on
an improved model of communication among agents, by including a model of speech intelligibility incrowds, and the use of voice to change the evacuation-start behavior. This allows the model to represent
the effects of evacuees calling to each other, and of automated emergency broadcast systems. To validate
our model, we simulate hypothetical scenarios using data from the Great Hanshin-Awaji Earthquake and
the Great East Japan Earthquake and tsunami as the initial conditions. Evacuation start and completion
rates from the simulation were similar to those observed in the original events, based on data frompost-disaster surveys, and showed the model’s ability to serve as a policy modeling tool.
Keywords: Simulation, Evacuation, Multi-Agent Systems, Information Exchange
1. INTRODUCTION
Natural disasters such as earthquakes and tsunamis cause enormous harm to people and damage toinfrastructure of highly populated areas around the globe. The experiences of the Great Hanshin-Awaji
Earthquake in 1995 and the Great East Japan Earthquake in 2011 revealed that the most effective way tomitigate damage from such events is to prepare robust plans for disaster management and relief. One key
part of those plans is the evacuation of the civilian population from the affected area.
One issue with the development of disaster management plans is how to evaluate and compare theeffectiveness of different policies. Evacuation simulators are a key tool in this task. A good simulator
reveals the effects of changes in disaster management policies across a wide array of variables, such as thenumber of evacuees, intensity of the disaster, and local conditions. This allows planners to quickly try
multiple variations of the desired policy, without incurring the cost of full evacuation drills.
However, because of the unpredictability of human behavior and the highly complex nature ofnatural disasters, the development of useful and accurate evacuation simulators is an ongoing area of
research. Accordingly, research has focused on the development of tools for the reproduction ofevacuation scenarios, both for earthquake and tsunami disasters (Oki and Osaragi, 2016, Muraki and
Kanoh, 2007, Clerveaux et al., 2008, Mas et al., 2012). In these studies, the authors try to bring theevacuation simulation closer to reality by studying and implementing a multitude of factors that contributeto the verisimilitude of the simulation, such as the effects of collapsed buildings on the walking speed of
evacuees, the psychology of when an evacuee decides to actually start evacuating, the walking speed anddensity of evacuees, and so forth.
In this study, we go a step further in this direction, and consider an earthquake and tsunami
evacuation simulation where evacuees and emergency services personnel can exchange information about
the location of evacuation areas by talking to each other or by shouting over greater distances. This
exchange of information influences how the evacuees decide their routes to the evacuation centers, and179

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how they decide whether to begin evacuation. This addition reflects existing surveys on the behavior ofevacuees in large-scale disasters, which indicate that communication between neighbors and nearby
evacuation staff is an important factor in the evacuee’s decision-making process (Cabinet Office, 1999,2012).
Our model is based on an agent-based simulation system that focuses on three types of agents:
Evacuees, Evacuation Leaders and Wireless Broadcast Systems (WBS). The three types of agents can
exchange information about the location of evacuation zones, and keep track of what information they
have, and how old it is.
Using the proposed model, we perform evacuation simulations on hypothetical scenarios based on
Kobe City during the Great Hanshin-Awaji Earthquake, and Ishinomaki City during the Great East Japan
Earthquake and subsequent tsunami. These scenarios use start conditions based on survey and census dataof these two historical disasters. We compare the final evacuation ratio, the average evacuation time, and,for the tsunami scenario, the start of evacuation behavior to the observed historical data. Additionally, weshow how this model could be used for the analysis of disaster management planning by performing asimulation of the Kobe City scenario using Evacuation Leaders and Wireless Broadcast Systems, whichwere not available during the original evacuation.
2. RESEARCH BACKGROUND
2.1 The role of communication in evacuation procedures
Local governments conduct studies and surveys after large-scale natural disasters to identify successes andfailures in its current disaster management policies. In the evacuation following the Great Hanshin-Awaji
Earthquake in 1995, the government survey identified the following issues (Cabinet Office, 1999):• Many evacuees went to evacuation shelters different from those designated by their residence,leading to delays in assistance from municipalities.
• Some evacuees rushed to the disaster response center instead of their designated evacuation
shelters, which hindered the emergency management activities by the municipalities.
• The communication of safety information to the residents was limited, due to the lack of a fixed
broadcast system such as the Disaster Wireless Broadcast System (WBS).
According to a different evacuee survey (Foundation for the Center of Fire Fighting Science, 1997),
the lack of information regarding evacuation shelters was a common complaint among evacuees. Fromthese surveys, it is clear that a disaster mitigation plan must consider how to effectively transmit
information to the population.
After the Great East Japan Earthquake of 2011 and subsequent tsunami, the government’s survey
showed that evacuees strongly felt that local governments needed to increase their efforts to spread
emergency evacuation information (Cabinet Office, 2012). The suggestions included activities such as
direct announcements to evacuees, the use of mass media and better use of the Disaster WirelessBroadcast System (WBS) (Inoue, 2012).
From these surveys, we can see that there is room for improvement in the disaster mitigation
practices from municipalities, in particular regarding the dissemination of information to evacuees. This
raises the issue of how to evaluate proposed changes to evacuation policies. One way to evaluate a new
evacuation plan or policy is to perform a large and costly disaster evacuation drill in the real world. This is
not feasible in the early planning stages when multiple competing plans are to be compared against eachother. Therefore, there is interest in the development of software systems that allow an inexpensive early
evaluation of new policy proposals using only computer simulations.
We also observe from these surveys the importance of communication channels to the success ofan evacuation effort. In the Great Hanshin-Awaji Earthquake, evacuees had to obtain information about
the rescue and shelter efforts from nearby people, or from portable radios. In the Great East Japan
Earthquake, it was reported that a number of people only began to evacuate after being prompted byfamily, neighbors or even in some cases passers-by who were calling out for people to evacuate from the180

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incoming tsunami (Cabinet Office, 2012). These points motivate us to take special interest in how thecommunication between evacuees and evacuation personnel is approached in evacuation simulation
models.
2.2 Multi-Agent Systems for evacuation simulations
Multi-Agent Systems (MAS) are models composed of multiple intelligent individuals (agents) working on
the same environment. After over three decades of research, MAS now come in many different forms,
including homogeneous and heterogeneous agents, cooperative or competitive, with or without
communication, and various degrees of complexity (Stone and Veloso, 2000).
MAS are extremely powerful and versatile systems for simulating social structures. For example,
Bosse et al. (2013) introduced a MAS that simulates a city and its inhabitants, and used it to analyze
patterns and mechanisms in crime rate, such as population density and police enforcement. In a similarline, Melo et al. (2005) used MAS to simulate and investigate effective police patrol routes.
In order to be able to perform such simulations, it is necessary to understand the workings ofcrowds. Borve (2014) surveyed various MAS studies that investigate crowd mechanics, with particularattention to the importance of crowd density. They highlight the use of these simulations for the analysis
of human stampedes, riots and evacuations. More recently, Genter and Stone (2016) considered indirectcommunication between agents, by showing how it is possible to influence the behavior of a flock byinserting “leading” agents that do not communicate directly with the rest of the flock.
The communication between agents in a simulation is a key component in many of these studies.
The level of abstraction in the communication model varies greatly. Pynadath et al. (2016) showed asimulation of an Anthrax attack in an American city, and focused on how the exchange of informationinfluences an agent’s beliefs about their environment. In their study, the exchange of information between
agents is abstracted to always occur correctly and perfectly. Eck and Soh (2015) proposed a simulation of
rescue robots in a disaster scenario. In this case, communication also happens instantly and correctly, but
has an associated cost, and the information obtained is not always up-to-date, so the agents may consider
exploring the environment instead of exchanging information about it. On the other end of the spectrum,
Cafaro et al. (2016) studied the effect of interruption in the exchange of messages (discussion) between
two agents. Here each phrase exchanged by the two agents and their timings is represented in the model.
In recent years, MAS and agent-based simulations have been used extensively in the simulation ofdisaster, evacuation and rescue scenarios. Ren et al. (2009) lists a large number of studies where thesimulation of evacuees was used to understand and prepare for disasters. We see two main groups ofapplications for disaster simulation: applications that focus on the rescue personnel, and applications that
focus on the evacuees.
In the first group, agents represent rescue workers, or sometimes robots. Morishita et al. (2002)
and Noda et al. (2002) are two examples of studies in this group. Both of them pay special attention to thecommunication protocol among the rescue agents in order to guarantee a well-coordinated rescue
operation. Beck et al. (2016) described a MAS for planning search and rescue in the wake of the recent
Haiti hurricane. Singh et al. (2011) reviewed many other studies in this group, and highlighted theimportance of communication among rescue personnel.
In the second group, agents represent evacuees, although sometimes evacuation personnel agentsare also included. Shinoda et al. (2002) discussed that one of the main problems of simulations focusing
on evacuees is that they require a number of agents’ orders of magnitude above simulations focusing only
on rescue personnel agents. To reduce this burden, many studies try to find efficient ways to model partsof the evacuation. The movement of crowds is a common concern. Boukas et al. (2015) and Sirakoulis(2014) used cellular automata to represent the crowd, while Yamashita et al. (2014) represented themembers of a crowd as individual agents and studied the effects of density on the evacuee’s movement
speed. Oki and Osaragi (2016) described a system to reflect how the destruction of buildings due to firemight block evacuation routes. Recently, Wolshon et al. (2015) introduced a very large-scale traffic
simulator to study highway policies during major hurricanes in the United States. Their system could
simulate over half a million vehicles, by using individually simple agents.
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Regarding the simulation of tsunami evacuations, Clerveaux et al. (2008) proposed a tsunami
evacuation simulator and discussed the importance of information transmission between evacuees andevacuation personnel, due to the very short timeframes involved. However, their study does not specify towhat degree the information exchange is modeled in the simulator. Mas et al. (2012) also proposed atsunami scenario simulator. Their study considers the necessity of quick evacuation in tsunami scenarios,and models the conditions when evacuees decide to actually begin evacuation using a stochastic behavior
function.
In this context, we are interested in developing a simulator for earthquake and tsunami evacuation
scenarios based on a Multi-Agent System, where the focus is on the simulation of pedestrian andcar-based evacuees as agents, and on the interactions between the evacuees and the evacuation personnel,
including the exchange of information through voice.
This study is based on the agent-based simulation system proposed by Muraki and Kanoh (2007).
Their system uses MAS to represent evacuees, and car navigation system data to allow the simulation ofcity-sized evacuation areas with tens of thousands of agents. In this study, we extend their system inseveral ways: inclusion of tsunami evacuation scenarios; message exchange among agents through voice,including an intelligibility model that limits the exchange of messages in crowded areas; agent
neighborhood familiarity, where some agents do not know the location of the nearest vacant shelter, andneed to receive this information from evacuation personnel or other evacuees; and evacuation startbehavior, which can change based on the exchange of messages between agents (representing people
urging others to evacuate).

Figure 1: How agents acquire Evacuation Route Information (ERI). In Step 1, agent (a) that does not have
ERI shouts a request. In Step 2, agent (b) hears the request and shouts a response. In Step 3,
agent (c), that also does not have ERI, gets information from another agent in the same cell. In
Step (4), the agents update their path finding based on the ERI received. Note that the model
implements “hearing” and “shouting” as the agents writing or reading messages in theirrespective cells.
3. PROPOSED MODELS
Following the ideas described in Section 2, we propose an evacuation simulation model that considers themutual exchange of information among agents regarding evacuation routes and the location of shelters.
This model could be used to test plans regarding the number of search and rescue personnel, and thedeployment of Disaster Wireless Broadcast Systems (WBS). It could also be used to study and understandhow information is disseminated considering the changing conditions of a tsunami-afflicted area.
Our model is based on the simulator proposed by Muraki and Kanoh (2007). This model represents
evacuees as agents, and the roads and pathways as cells on a grid. Buildings in this model are abstracted as
properties of the region lattice that divides the simulated area.
One agent in the simulation represents a group of one or more people, and one cell in the grid
represents a section of road that can contain one or more agents and obstacles (debris from the natural
disaster). At each time step, which represents one second in real world time, the agents move towards theirevacuation destinations following their path finding rules.
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Using Muraki’s model as a base, we add the ability of the agents to exchange evacuation routeinformation, and the ability of tsunami damage to affect the available evacuation routes in the simulation.
These additions are summarized in the following list:
Addition of Evacuation Leader (EL) and Wireless Broadcast System (WBS) agents.
Each agent can add information it has about the environment to the limit of its voice.
Each agent can request and acquire information about disaster shelters from the environment.
The evacuee agent’s path finding rule is influenced by the information about disaster shelters.
Each evacuee agent has an “intent to evacuate” attribute, which determines when it actually
starts the evacuation process. Intent to evacuate is modified by receiving information from otheragents.
• Addition of “height” and “tsunami damage” properties to cells, which influence path finding.
• Addition of a mechanism of spreading tsunami damage to the simulated area.
•
•
•
•
•

The information exchange model used is described in Fig. 1. Specifically, agents place requests
and offers for Evacuation Route Information (ERI) as a sound message in their current cell. The cell
propagates this message to neighboring cells, based on the voice message sound intensity. Other agents
can then pick up messages in their own cells, and use those messages to update their internal state.
In our model, the ERI is assumed to contain information about the location and availability of evacuation
shelters. This information is qualified by its newness and source, so that evacuees prefer newer ERIs, andthose that come from official sources (EL and WBS) rather than those that come from other evacuees.

Attribute
Resident
Familiar
Weak
NumberCar
AsoundInterval
Current cell
Moving Speed
Has ERI
Current ERI
Sasking
Sproviding
Intention
Evacuation Start

Table 1: Attributes of the Agents
Description
Flag: This agent lives in the area
Flag: Agent knows designated shelter
Flag: Agent includes elderly or children
Integer: People in this group
Flag: Agent is moving by car
Number: Sound broadcast intensity
Number: Time steps for ERI request
Pointer: Agent’s current cell
Number: Agent’s current speed
Flag: Agent received ERI information
Pointer: Time and source of ERI
Flag: Agent is currently requesting ERI
Flag: Agent is currently providing ERI
State: Condition for evacuation start
Time: Time stamp for evacuation start

Agent types
Evacuees
Evacuees
Evacuees
Evacuees, EL
Evacuees, EL
All agents
Evacuees
All agents
All agents
Evacuees
All agents
Evacuees
All agents
Evacuees
Evacuees

3.1 Agent types
The proposed model defines three types of agents: Evacuees, Leaders, and Wireless Broadcast Systems.
Evacuee: The evacuee agent represents one or more people evacuating towards their destination.
The agent destination depends on their internal state (Section 3.4). This state includes whether they
are on foot or in cars, whether the group includes “weak walkers” (such as elderly or children),whether the group is a resident or commuter, and whether it is familiar with local evacuation•

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information.
• Evacuation Leader (EL): The EL agent represents a staff member from the municipal rescue
efforts. During the evacuation, it will look for evacuee agents to disseminate evacuation shelter
information. It is equipped with a loudspeaker and a radio, so it always has up-to-date ERI data.
• Wireless Broadcast System (WBS): The WBS agent represents a system of fixed radio
broadcast poles that exists in most municipalities in Japan. During an evacuation, it will broadcast
ERI over a large area at regular intervals.
The agents are defined by the attributes listed in Table 1. These attributes define the agent’s ability
to move, transmit information to other agents, and its decision-making process regarding path finding andstarting the evacuation. Some of these attributes are common to all agents, while others are specific toevacuee agents. While the exact behavior of the agent depends on its type (Evacuee, EL or WBS), thegeneral behavior of an agent is described by Algorithm 1. In this algorithm, t is a time stamp for
simulation iterations, which begins at zero, and where each iteration corresponds to one real world second.
Algorithm 1 Agent update algorithm
Require: Current simulation time t
1: if t%15 == 0 then2:
Request/Provide ERI. Sec. 3.2

3: end if
4: if t ≥ Evacuation Start then5:
if (Car is TRUE and t%2 == 0) or (Car is FALSE and t%15 == 0) then6:
Test for evacuation start and completion
7:
Update Agent’s Location and destination
8:
end if
9: end if. Sec. 3.3. Sec. 3.4

3.2 Agent ERI exchange behavior
Following the rules in Algorithm 1, every 15 simulation steps an agent tries to provide/receive ERI
information from the environment. While the transmission of this data across distances is handled by thecell model (see Section 3.5), the transfer of data to and from agents is handled by the agent model. Figure
1 gives an overview of this process.
Evacuee agents first try to acquire information from the cell (EL and WBS agents are assumed toalways have the newest information). This process takes the following steps:
・Agent pools the local data from the cell (Infolocal) with probability plocal, if available. This corresponds toagents in the same cell sharing information.
・Agent pools regional data from the cell (Infoprop), if available. This corresponds to agents overhearing
information.
・Agent compares any information that it currently possesses with the local and regional data, and keeps
the newest one.
・If the agent has no information, it sets its own Sasking flag to true, indicating to the cell model that theagent may send out an information request.
As a result of this process, the agent may obtain an up-to-date ERI, which will be used to define
the agent’s path finding (see Section 3.4) and also when the agent begins evacuating (see Section 3.3).
After the agent has acquired information, it will determine whether it is broadcasting its own information.
This is controlled by the Sproviding flag, which is turned on according to the following conditions:
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・Evacuee agent: will set Sproviding to true if it hears an information request (represented by cell’s attribute
Sasked).
・EL agent: will set Sproviding to true if any cell within 50m of its location contains at least one evacuee
agent.
・WBS agent: will set Sproviding to true at fixed intervals, according to the simulation parameters.
3.3 Agent evacuation start behavior
Before moving, an evacuee agent will test whether it should begin the evacuation process, and then test
whether it has completed the evacuation process.
The test for beginning the evacuation process has two different patterns: “Start by time distribution”
and “Start by intention”. These two patterns were designed following the patterns observed in earthquake
and tsunami evacuations, respectively.
In the first pattern, the evacuation start time for each agent is fixed, following the distribution
defined in the scenario parameters. This reflects the situation of an earthquake evacuation where after theearthquake is finished, each evacuee will start to move according to their own conditions.
In the second pattern, the evacuation start time is controlled by the agents Intention attribute. The value of
this attribute is one of “evacuate immediately”, “evacuate tentatively” and “do not evacuate”, and eachvalue determines a range of possible start times. The distribution of intention values, and the evacuation
start time associated with each value are determined by the scenario parameters.
An agent’s intention attribute can be modified when the agent receives information from otheragents. In other words, nearby evacuees or EL may call this agent to start evacuation, or the WBS may
cause the agent to begin evacuating. In the model, whenever an agent that has not begun evacuatingreceives information from other agents, it may change the value of its Intention attribute, following theprobabilities listed in Table 2.
To test whether the evacuation is complete, an agent checks whether its current cell corresponds to
its intended evacuation shelter. If it does, it checks whether the evacuation shelter is full.
If the evacuation shelter is not full, the agent will enter the “evacuation complete” state, which stops any
further state changes, and also stops the sending or receiving of voice messages. If the evacuation shelter
is full, the agent will update its ERI, and change its destination to the nearest non-full evacuation center.
Table 2: Probability of change in the intention attribute of an agent when it receives an ERI from otheragents. Data based on the 2012 survey from the Japanese Cabinet Office (Cabinet Office, 2012)
Source of ERI
WBS (only first time)
Evacuation Leader
Evacuee (first time)
Evacuee (after first)

Change on Intention value
Ev. Immediately Ev. Tentatively No Change
0.4
0.4
0.3
1
0
0
0.4
0.45
0.15
0.1
0
0.9

Table 3: Movement and Path Finding Patterns for Evacuee Agents
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Scenario

ERI
Yes

Earthquake
No
Tsunami

Yes
No

Agent
Cell
Pattern
Resident Familiar Cellfollow Destination
Path finding
Yes–
DijkstraYes
No–
Shelter
RTA*
No
–
–
Yes
Cellfollow––
No
Random––Shelter or High
DijkstraPlace (Closest)–
High Place

3.4 Agent movement and path finding
An agent’s movement is divided into two steps: calculating the agent’s destination, and then calculating
the agent speed. WBS agents are static and do not move. Evacuee and EL agents use the same rule forcalculating agent speed, but different rules for path finding.
(1) Destination and Path finding
Evacuation Leaders try to find and guide evacuees. Therefore, they will alternate between two states: theagent will move for 5 to 30 minutes, and then stay in place between 0 and 20 minutes. While it is moving,
the agent will keep going in the same direction, until it reaches an intersection, when it will choose a new
direction at random. In the case of tsunami scenarios, directions that lead to higher elevation have a higher
weight in this selection, and this weight increases as the simulation progresses.
Evacuee agents’ path finding will depend on whether they possess ERI, how familiar they are withthe area, and whether it is a tsunami or earthquake evacuation scenario. These cases are summarized inTable 3.
If the agent has no information about evacuation shelters at all, it will follow other agents in thesame cell, or move randomly. If the scenario is a tsunami evacuation, it will move towards a higher placefollowing the Dijkstra algorithm. Agents moving randomly will have a higher weight towards map areas
with high building density, to simulate the observed tendency of evacuees to move to places that arefamiliar to them (Kashiwabara et al., 1998).
Resident agents will move towards an evacuation shelter, while non-resident agents can move to an
evacuation shelter, or to the outer borders of the map (during the tsunami scenario, the “map border”
option is not available).
Finally, agents familiar with the region will use the Dijkstra algorithm to choose their path to thedestination, while agents without familiarity will use the Real Time A-star (RTA*) algorithm.
(2) Movement Speed
After the EL or Evacuee agent chooses its destination, and consequently which cell it wants to move tonext, the simulation calculates the agent speed, and whether the agent successfully changes cells in thissimulation step.
The agent speed is decided based on: The average crowd density (Fruin, 1987) between the cell theagent is in, and the cell it wants to move to, the number of people represented by the agent, whether theagent is in a car, whether it includes “weak walkers”, and whether the agent has ERI. If the agent is in acar, the speed calculation also takes into account whether there are obstacles (road damage) in the cell.
Given these attributes, the agent’s speed is calculated using Algorithms 2 and 3, resulting in a final speed
between 0 and 1.43 m/s for pedestrian agents, and 0 to 40 km/h for driving agents.
Once the speed of an agent is found, the simulation calculates how many cells the agent can move towithin 15 (for pedestrians) or 2 (for drivers) simulation steps, and updates the agent’s current cell186

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accordingly.
Algorithm 2 Walk speed algorithm
Require: Cell Density cd, Agent attributes Weak, Has ERI, Number
1: speed = max(1.433 − 0.417 ∗ cd,0)2: if Weak then3:
speed = min(speed, 0.94)
4: end if
5: if cd > 0.67 then6:
if not Has ERI then7:
speed = speed*0.75
8:end if9:
if Number > 1 then10:
speed = speed*(0.95 - 0.025*Number)11:
end if
12: end if
Algorithm 3 Car speed algorithm
Require: Cell Obstacle co1: speed = 40000 / 3600
2: if First time agent is moving then3:
speed = 30000 / 3600
4: end if
5: if Current cell is a curve or Evacuation Shelter then6:
speed = 10000 / 3600
7: end if
8: if Current cell has an obstacle then9:
speed = 5000 / 3600
10: end if
3.5 Environment
The simulation environment is organized into a regional grid and the local cells. The regional gridcontains large-scale information about the simulated environment, such as height, type and density ofbuildings. It is also used to calculate the spread of tsunami damage across the map. The local cells
represent small stretches of connected roads. In the simulation, agents only move within the local cells.
For the generation of regional and cell data, our model uses the map database and regional file data fromthe Car Navigation Research S Standard format, Version 2.2 (Navigation System Research Group, 1997).
The list of configuration parameters for regional and local data is summarized in Table 6.
3.5.1
Regional Grids
The simulated area is divided into an x × y lattice, where x and y are arbitrary integers. For each region in
the lattice, we define its population, building type and inundation levels as input parameters in the model.
The population parameters (number of inhabitants, proportion of residents, weak walkers, vehicle users,etc.) are used to define the initial setup of the evacuee agents. In the simulations discussed in this study,
we obtain this data from the local census (See Section 4 for details).
The building type parameter is used to determine the presence of debris (damaged buildings)
obstructing the cells. Following the model of Muraki and Kanoh (2007), each region in the lattice has a187

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building type value. At the start of the simulation, each cell in the region has a chance of having
obstructions depending on the building type of the region (see Table 4). These obstructions reduce theeffective area of the cell.
Table 4: Regional Building Types and Chance of Obstruction in Cells
Building type
0
1
2
3
4

Regional grid meaning
No buildings
Low buildings, Low density
Low buildings, High density
High buildings, Low density
High buildings, High density

Chance of obstruction at eachcell
No obstructions
10% chance
50% chance
1% chance
5% chance

Table 5: Cell attributes
Attribute nameCoordinates
Area
Neighbor
MapBorder
ShelterInundation Level
Population
Density
Cellfollow
SaskedPlocalInfoprop
Infolocal
Shelter Available

Attribute meaning
X and Y location of the cell in the regional map
Surface area of this cell available for pedestrians and vehicles
List of connecting cells
Is true if this cell borders the map
True if a shelter is adjacent to this cell
Index for the time when this cell is considered inundated
Sum of the people of all agents currently in this cell
Population / Area, in m2
Neighbor cell to which more than half the agents are moving
True if an information request has propagated to this cell
Probability of obtaining ERI indicated in Infolocal
ERI that was propagated by a broadcasting agent
ERI that is most common in this cell
True if an adjacent shelter can receive more evacuees

In addition to Muraki’s model of building damage, we also include tsunami damage in thesimulation. Each region is assigned a height, and an inundation level, and the simulation as a whole has a
global inundation value. The global inundation value begins at zero and increases at pre-determinedintervals. Cells in regions with an inundation level equal to or less than the global inundation value areconsidered inundated and inaccessible. To calculate the speed at which the regions and cells become
inundated, we used recorded evacuation and disaster data (Ishinomaki Municipal Government, 2014).
3.5.2 Local Cells
A cell in our model represents a 20-meter road region that can be traversed by cars or pedestrians. In thissense, highways and roads exclusive to cars are not represented. Also, buildings are represented at theregion level, not the cell level.
All cells have information regarding which other cells are adjacent. They may also contain an
adjacent evacuation shelter (which has a maximum number of people and vehicles that it can handle
before becoming full). Also, cells may be flagged as “border” cells, and be eligible as evacuation shelters
for agents representing commuters who live outside the area defined in the scenario.Other attributes of a188

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cell, such as the number of people currently in it, and whether any ERI can be heard from this cell, arelisted in Table 5.Every 30 simulation steps the cell attributes are updated according to the following steps:
Receive ERI requests and offers from agents; Propagate sound messages across cells; Update cell
attributes.
・Receive ERI requests and offers: An evacuee agent will generate an ERI request if its Sasking attribute istrue, and if the crowd density of this cell is less than 0.05/m2. After the request, Sasking is set to false.
An agent will generate an ERI offer if the attribute Sasked of this cell is true (i.e., a request can be heard).
Also, EL and WBS agents will generate ERI offers following the rules described in Section 3.2.
・Propagate sound messages: Each cell will try to propagate the messages generated in the previous stepto other nearby cells. The probability pmij of a message m at cell i to be transmitted to cell j is given by
Equation 1, which is truncated to the (0,1) range. This formula assumes a point sound source, with noacoustic reflection or diffraction. It is based on experimental results on word intelligibility in crowds(Kondo et al., 2007) and average noise intensities at crowded downtown locations (Japanese Ministry of
the Environment, 1998, Mitsuoka, 2005).
Table 6: Initial Scenario Configuration Parameters
Scope
General

EvacueeEL agent
WBS agent
Cell
RegionParameter
Simulation starting time
Tsunami arrival time
Inundation level increase interval
Total number
Proportion of residents
Proportion of regional familiarity
Proportion of weakened walkers
Proportion of evacuation by vehicle
Evacuation Starting Time
Total number
Starting time of activity
Total number
Placement
Placement of evacuation shelters
Shelter vacancy limit
Population density
Building type
Height level

(1)
amij
= Asoundm − 20log10 dij
cj: crowd density of cell j people/m2
dij: Euclidian distance between cells i,j (meters)mAsound: voice capacity of agent m in decibels (db)
In this equation, the value of the Asound parameter is set as 85 for the evacuee agent, 105 for the
Evacuation Leader agent, and 115 for the WBS agent. These values make the assumption that the EL agent
is equipped with a loudspeaker or loudspeaker vehicle. It is worth noting that in the current model,
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buildings and obstructions do not affect sound transmission directly (however, obstacles affect it indirectly
by increasing the crowd density of a cell).
・Update attributes: The cell updates its attributes according to the following rules:–
If the cell receives one or more ERI requests, it sets Sasked to true.
–
If the cell receives one or more ERI offers, it sets the value of Infoprop to one of them,selected randomly. Otherwise, this value is set to none.
–
Plocal is set to the proportion of agents in the cell that possess ERI information.
–
If Plocal is greater than 0, Infolocal is set to the ERI of one of the local agents, selected
randomly.
–
Population, Cell Density, Cell Follow and Shelter Available are updated according to
the number and attributes of agents in the cell.
–
Inundation status is updated according to information from the region in which this cell
is located.
Table 7: Details of the Agent Composition for the Two Simulated Regions
Model Attribute
Number of evacuees
Number of agents
Weak walkers

Evacuees in car
Non-residents
Familiar residents

Number of evacuees

Number of agents
Weak walkers

Evacuees in car
Non-residents
Familiar residents

Value
Data Source
Kobe city scenario
43,998 National Census (Statistics Bureau of the Ministry ofInternal Affairs and Communications, 2010)
14,66643%
National Census (Statistics Bureau of the Ministry ofInternal Affairs and Communications, 2010), people
less than 15 or more than 65 years old.
20%
Post-evacuation survey (Kashiwabara et al., 1998)
20%
Assumption based on transit at 6:00 am
54%
National Census (Statistics Bureau of the Ministry ofInternal Affairs and Communications, 2010), people
who have lived in the same area for more than 5
years.
Ishinomaki city scenario
104,271 National Census (Statistics Bureau of the Ministry ofInternal Affairs and Communications, 2010), andDisaster Prevention Plan (Ishinomaki Municipal
Government, 2014)
34,75743%
National Census (Statistics Bureau of the Ministry ofInternal Affairs and Communications, 2010), people
less than 15 or more than 65 years old.
50%
Post-evacuation survey (Cabinet Office, 2012)0%
Not considered for this simulation
60%
Post-evacuation survey (Cabinet Office, 2012)

4. MODEL EVALUATIONIn this section, we present simulation experiments to observe the proposed model, and evaluate how it
performs as a representation of a natural disaster evacuation.
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In these experiments, we use hypothetical scenarios based on the observed initial conditions of theevacuations in Kobe city during the Great Hanshin-Awaji Earthquake, and in Ishinomaki city during theGreat East Japan Earthquake and tsunami. These initial conditions are set based on demographic data fromthe national census and post-evacuation surveys (see Table 7).
For each experiment, we change the voice information exchange parameters or the evacuation startbehavior parameters, and observe how the change of these parameters affects the end state of thesimulation. Namely, we observe the time to start the evacuation, the end time of the evacuation, and thenumber of completed evacuations. In the first two experiments, we compare these values to the observed
values in the post-evacuation survey, to consider to what degree the simulated outcomes agree with theobserved data from the real-world evacuation.
In the third experiment, we modify the hypothetical scenario based on the Great Hanshin-Awaji
Earthquake evacuation, adding Evacuation Leaders and Wireless Broadcast System infrastructure, whichwere not deployed during the original disaster. The objective of this experiment is to demonstrate how the
proposed model could be useful as a way to measure the effect of new disaster mitigation strategies, and toassist in the design and decision of new policies.
A summary of the experiments and their results is listed in Table 8. To understand the internalvariance of the proposed model, for each experiment we repeat the simulated scenario 30 times. In thefollowing sections we report the averaged results along with their standard deviation. We report statisticalsignificance when relevant in the text, using Bonferoni corrections when comparing multiple quantities.
These statistical significance tests are useful to observe the influence of the inherent variance of stochastic
multi-agent systems, but do not necessarily take into account uncertainties related to the limitations of thesimulated model (which are discussed in Section 5.1).
Table 8: Summary of the Experiments and Results
ExperimentControlled parameters
Results summary
Experiment I, Section 4.1: Kobe City – Analysis of route information exchange
Case 1-1
No route information exchanged
Case 1-3 shows a higher evacuation
rate, closer to the expected value. Case
Case 1-2
ERI exchanged in the same cell
1-2 shows higher variance, while 1-3
Case 1-3
ERI exchanged across cells
seems to reduce the randomness of themodel (Table 9)Experiment II, Section 4.2: Ishinomaki City – Analysis of evacuation start behavior
Case 2-1
Evacuation-start: 0-10, 5-15 minCase 2-3 shows the best agreementwith the observed data, in terms ofCase 2-2
Evacuation-start: 5-15, 10-20 min
evacuation start time, and totalCase 2-3
Evacuation-start: 10-15, 15-25 minevacuation time (Figures 4 and 5)
Experiment III, Section 4.3: Kobe City – Introduction of EL and WBS
Case 1-3
No communication agents
The introduction of WBS agents
showed a reduction in average
Case 3-1
EL agents only
evacuation time. The contribution
Case 3-2
WBS agents only
from EL agents was unclear (Table 11)
Case 3-3
Both agents191

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Figure 2: Left: Area for the hypothetical scenario in Kobe City. Blue circles indicate designated shelters,
purple circles volunteer shelters. Right: Evacuation Rate over time for four shelters in thesimulation Case 1-3, and two post-disaster survey results.
4.1 Validation experiment I: Kobe City
For this experiment, we use a 11 km×9 km area around the district of Kobe City, as illustrated in Fig. 2.
We use data based on the post-disaster report (Kashiwabara et al., 1998) to determine the location andquantity of evacuation shelters, the number of evacuees, and their starting evacuation times. Other
regional parameters of the simulation were set based on the Japanese national census (Statistics Bureau of
the Ministry of Internal Affairs and Communications, 2010), and the city plan drawing of Kobe city (Kobe
City Planning Division).
Data from the municipal government states that there were 28 “designated” evacuation shelters.
Additionally, there were reports of “voluntary” shelters formed by the evacuees (Kashiwabara et al., 1998).
Because there is no exact data about the voluntary shelters, we simulate them by adding 60 shelters at
random residential areas in addition to the 28 designated shelters.
Following the post-disaster report, our simulation includes 44000 evacuees during the period between
6:00 and 24:00 on January 17th, 1995. We define evacuation starting times for the evacuee agentsaccording to the following rule:
25% of the agents begin evacuating immediately following the earthquake. Evacuation start timeis drawn from a normal distribution (µ = 6:30,σ = 0:10).
• 25% of the agents begin evacuation a few hours after the earthquake. Evacuation start time isdrawn from a normal distribution (µ = 9:00,σ = 1:00).
• 50% of the agents begin evacuation late in the afternoon. Evacuation start time is drawn from anormal distribution (µ = 16:30,σ = 2:30).
•

For this scenario, it was reported (Kobe City Fire Department, 1995) that Kobe municipality did
not set up radio broadcast stations (WBS), and the loudspeaker cars of the fire brigade were being used for
rescue activities. Therefore, we consider only the communication between evacuee agents. We simulate
the evacuation using three different information sharing models:Case 1-1: Evacuees do not share information.
Case 1-2: Evacuees share information with others in the same cell.
Case 1-3: Evacuees share information with others in the same cell, and use voice tocommunicate with agents in nearby cells.
•
•
•

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Case 1-1 is the same as the original Muraki Model that we use as the base for our proposed model.
Cases 1-2 and 1-3 include the changes to the communication model described in this study. The results ofthese simulations are summarized in Table 9 and Fig. 2.
Table 9: Simulation results for the Kobe City validation experiment. An average of 30 simulations isreported, with its standard deviation in parentheses.
Case
1-1
1-2
1-3

(Muraki Model)(Same cell communication)(Voice communication)

Evacuation Ratio
92.2 (0.28)
93.9 (1.68)
95.5 (0.59)

Evacuation Time
27.8 min. (0.41)
28.4 min. (1.41)
27.3 min. (1.18)

The results of Table 9 show that as we add communication abilities to the evacuee agents, we get ahigher evacuation ratio without an equivalent change in the average evacuation time. In this hypotheticalscenario, since the initial number of evacuees was not available, we defined the initial number of agents tobe the same as the number of evacuees who reached shelters according to the evacuation records.
Therefore, our goal is to find a parameter set that brings the simulated evacuation rate to be as close to100% as possible. In this sense, Case 1-3 seems to be the most appropriate parameter setting to make thishypothetical scenario closer to the observed data. By modeling the spread of information about evacuationroutes, our model can represent the initial confusion from evacuees, followed by a more organized
movement towards the shelters as the agents exchange information.
In Fig. 2 we see the graph of the rate of evacuation completion over time for Case 1-3. The lines
represent the results of four simulated shelters (indicated by numbers in Fig. 2), while the points represent
data from post-disaster surveys. It is interesting to note that we can infer the shelter density from thesecurves: Shelters 1, 2 and 3 are in areas with low shelter density, and they receive over 50% of theirevacuees over the first 5 hours of the simulation. Shelter 4, on the other hand, is located in a region withmultiple voluntary shelters. So, for the first 12 hours, it receives less than 30% of its capacity, and thenquickly fills up as the other shelters around it become full. This indicates how the simulator could be used
to find areas that have a lack or excess of evacuation shelters.
It is interesting to also consider the higher variance of Case 1-2. Figure 2 shows that the evacuation
speed and completion ratio change with time. As more and more evacuees get off the streets and into
shelters, the effectiveness of information exchange varies as well. At the beginning of the evacuation,
there is a higher number of evacuees on the move, and information exchange occurs often. As theevacuation progresses and agents reach their destinations, the number of agents on the streets is reduced,
and exchange of evacuation route information depends on whether two agents happen to be in the samecell. Using voice propagation in Case 1-3 makes it easier for agents to contact each other even if they are
relatively far apart, and therefore makes it more likely that late agents will obtain some sort of evacuationroute information, reducing the randomness of the model.

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Figure 3: Left: Simulated area in Ishinomaki City. Circles indicate shelters, while squares indicate
WBS positions.
Right:Inundation levels used in the simulation. Numbers indicate the order in which each zone
becomes inundated.
4.2Validation experiment II: Ishinomaki City
For this experiment, we use a 11 km×10 km area around the high population zone of the former
Ishinomaki district in Ishinomaki city, as shown in Fig. 3. This city was one of the most affected by theGreat East Japan Earthquake and its ensuing tsunami. Using numbers derived from the disastermanagement operation based on Ishinomaki City (Ishinomaki Municipal Government, 2005), theevacuation scenario defines 68 evacuation shelters, 100 EL agents in cars, and 132 WBS agents. WBS
agents repeat their broadcast every 5 minutes for the duration of the evacuation process. Other regionalparameters are set following the 2010 national census (Statistics Bureau of the Ministry of Internal Affairs
and Communications, 2010).
The simulation generates agents for the people living or working in the area who were affected byinundation (inundation levels 1-4 in Fig. 3). In this figure, Area 0 represents the sea, Area 1 becomesinundated 30 minutes after the earthquake, and each subsequent area becomes inundated 2.5 minutes after
that, until Area 4 at 37.5 minutes. These values were obtained from the municipal report on the disaster(Ishinomaki Municipal Government, 2014).
Note that while an earthquake evacuation scenario, such as that described in Section 4.1, happens
over a period of 18 hours, a tsunami evacuation scenario is much shorter, in this case, under 40 minutes.
Therefore, the time when people begin evacuating is a critical issue in disaster mitigation for tsunami
scenarios. Accordingly, in this hypothetical simulation we evaluate both the evacuation start time, and theevacuation time across the agents in the simulation.
A survey with the evacuees for the 2011 tsunami (Cabinet Office, 2012) identified the following
behavior patterns of people regarding evacuation starting times:
20.5% of evacuees began evacuation immediately after the earthquake.
23.1% of evacuees began evacuation tentatively after the earthquake.
3.4% of evacuees began evacuation immediately after receiving information through radio
broadcasts.
• 4.5% of evacuees began evacuation tentatively after receiving information through radio
broadcasts.
• 48.5% of evacuees began evacuation after noticing the approaching tsunami.
In our model, these four patterns are represented through the “evacuation start” behavior in
•
•
•

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evacuee agents, described in Section 3.3. In this experiment, we want to see to what degree this model isable to reproduce the observed evacuation start patterns.
To this end, we focus on three parameter settings for the simulation. For each case we change thedelays associated with the “evacuate immediately” and “evacuate tentatively” behaviors according toTable 10. Note that, in addition to the initial evacuation start behavior, the model also allows agents tobegin evacuating earlier when they receive messages from other agents, as described in Table 2.
The simulation of each case is repeated 30 times. In Table 10 we can compare the evacuation ratio
between each case and the historical record. We see that Case 2-2 reaches a very close mean to the record,
but also has quite a large variance.
Table 10: Simulation results for the evacuation start behavior validation experiment. Results in the rightcolumn are the average of 30 simulations, with standard deviation in parentheses. Values in the middle twocolumns are the range of simulation start times for the two behaviors included in the model.
Case
Historical record
Case 2-1
Case 2-2
Case 2-3

Time to start Evacuation
Result“Immediately”“Tentatively” Evacuation ratio––
79.4
0-10 min5-15 min82.0 (0.48)
5-15 min10-20 min79.6 (4.19)
10-15 min15-25 min75.4 (0.30)

Table 11: Variation in simulation results according to the introduction of disaster preparedness policies.
Values in parentheses are the standard deviation of the results.
Policy introduced
Case 1-3 (No policy)
Case 3-1 (EL agents)
Case 3-2 (WBS agents)
Case 3-3 (Both)

Evacuation completion
ratio (%)
95.5 (0.59)
95.7 (0.22)
95.8 (0.19)
95.8 (0.20)

Average evacuation
time (min)27.3 (1.18)
27.7 (0.9)
25.1 (0.38)
25.1 (0.31)

To better understand these results, Figs. 4 and 5 offer a breakdown of the start evacuation time andthe total evacuation time, respectively, across the evacuee population. In these images, we can see that the
breakdown of Case 2-3 is closer to the survey record than that of Case 2-2. One way to explain thisdiscrepancy is to consider that the historical record on the evacuation ratio includes people who did notevacuate because their home was already in a secure location, while the evacuation ratio of the
hypothetical scenario considers only agents that both began evacuating at some point and thensuccessfully completed their evacuation. Taking these “non-evacuees” into account when interpreting
Table 10 leads us to believe that Case 2-3 provides the best reproduction of the available survey data.
We also note that while both Cases 2-2 and 2-3 do not include behaviors for agents to begin
evacuation before the 10-minute mark, 40% of the agents begin their evacuation between 0 and 10
minutes. This reflects agents that began evacuation initially by receiving information from either EL or
WBS agents, and eventually a cascading situation from other evacuee agents calling each other. As these
proportions approximate the survey data, it indicates that the proposed sound propagation model is a good
approximation of evacuees calling each other out during a natural disaster.
4.3 Planning experiment: Kobe City
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In this third experiment we want to demonstrate how the proposed model could be useful in the planningof disaster mitigation policies. To this end, we repeat the Kobe City Earthquake experiment, but add to it
three scenarios that do not correspond to the historical evacuation effort. Namely:•••

Case 3-1 Addition of Evacuation Leaders;Case 3-2 Addition of Wireless Broadcast Systems;
Case 3-3 Addition of both EL and WBS agents.

It was reported (Kobe City Fire Department, 1995) that during the Great Hanshin-Awaji
Earthquake, Kobe municipality did not have emergency wireless broadcast setup, and that the fire brigade
focused mainly on rescue activities, so they did not act as evacuation organizers. Therefore, in theseexperiments we consider the addition of each of these two systems. We are interested in observing theeffect of these changes in the evacuation ratio and the average evacuation time.
For Cases 3-1 and 3-3 we add two EL agents for every evacuation center, for a total of 172 agents.
These agents are deployed 4 hours after the start of the simulation, to coincide with the time that theregular rescue operations took place. For Cases 3-2 and 3-3, we deployed one WBS agent for every
evacuation center, for a total of 88 agents, which broadcast Evacuation Route Information every 5 minutes
after the earthquake.

Figure 4: Time to start evacuating across the population in the Ishinomaki City hypothetical simulation

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Figure 5: Evacuation time across the population in the Ishinomaki City hypothetical simulation
The results of this experiment are listed in Table 11. In this table, we compare the three proposed
scenarios with Scenario 1-3 of the first experiment (Section 4.1). None of the three proposed cases show adifference in terms of the evacuation ratio from the original setup without the deployment ofcommunication systems. However, we observe that Cases 3-2 and 3-3, which add WBS agents to thescenario, show a small reduction in the average evacuation time. The statistical analysis confirms that thisreduction is caused by the change in simulation parameters (with confidence above 95%).
Because of the limitations of the model, it is difficult to extract exact prediction information. For
example, in real life the Evacuation Leaders could decide to change their strategy dynamically according
to what they observe during the evacuation. However, we can infer from the results that even in such asimple simulation of human behavior, a system that provides information to the evacuees made theirevacuation efforts more effective. Regarding the Evacuation Leaders, a more complex agent might havebeen able to show a positive effect on the evacuation too.
In any case, this result shows that the distribution of information to evacuees is most critical during
the initial moments of evacuation (before they are able to eventually organize themselves). Thisobservation reflects the complaints and observations made after the earthquake, as discussed in Section
2.1.
With the proposed simulation model, a governmental body could use this result to justify an
investment in communication systems such as damage mitigation measures, and even perform further
investigations into the most effective areas for adding such a system.
5. CONCLUSION
Because of the difficulty of predicting earthquakes, currently the best policy for mitigating damage fromthese natural disasters is to maintain preparedness of the population and infrastructure. To this end,
simulation models of evacuations are an important tool for the planning and development of effective
damage mitigation policies.
In this context, we proposed a multi-agent simulation model for the simulation of an earthquake or
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tsunami evacuation. Considering the importance of the exchange of information between evacuees andevacuation personnel during a natural disaster, our Multi-Agent System uses a communication model
where: a) The agents exchange information by sending messages to each other over voice; b) Thesemessages influence the agent’s behavior regarding path finding and start-evacuation behavior; c) We use
Kondo et al.’s voice intelligibility model to determine the spread of information in crowded streets. With
these characteristics, we reproduce behaviors that have been observed during actual evacuations, such as
agents that do not begin their evacuation until someone else urges them to do so.
The proposed system is an extension of an existing earthquake simulator, introducing the conceptsof message exchange described above, as well as the simulation of time-dependent tsunami damage. To
evaluate the validity of our extended model, we performed experiments on three hypothetical scenarios,
based on the starting conditions observed in Kobe City during the Great Hanshin-Awaji Earthquake in
1995, and in Ishinomaki City during the Great East Japan Earthquake in 2011, and varying message
exchange parameters. We observed the initial and final states of the evacuation simulation, and comparedthem with observed data to validate the proposed model. Additionally, we studied the use of the proposedmodel as an analysis tool, by considering the use of Evacuation Leaders and Wireless Broadcast Systems
in the Kobe hypothetical scenario.
These results show that the proposed information exchange model, and the accompanying model
for changing agents’ evacuation-start behavior, are important components of a large-scale, multi-agent
evacuation simulation model. Therefore, we recommend the addition of the model described in this study
to such simulators, and its further study.
5.1 Model limitations and future work
Multi-agent simulation models involve uncertainty based on the decisions of what to represent, and what
to abstract in the model. Adding the representation of abstracted effects to the model may reduce these
uncertainties if the effects can be represented effectively, but will also increase the complexity of themodel.
For the model presented in this study, we highlight three abstracted effects that we believe could
effectively improve the precision of the model, and will be considered in future work. These are not the
only abstractions contained in this model.
First, the current model uses a proprietary car navigation map that, while it is very accurate forJapanese cities, is difficult to modify or use with arbitrary maps. This limits the current model to streetsthat are passable by cars, and prevents behavior such as evacuees crossing parks or pedestrian roads. One
way to fix this limitation would be to change the simulation model to use data from the OpenStreetMaps
foundation.
Second, our current agent model represents only a very small set of human agent strategies. For
example, it was observed during the evacuations that some evacuees employed a multi-stage evacuation,
first going to a temporary nearby safe location, and then moving to the proper evacuation shelter. Also, theevacuation leader behavior is largely random, while rescue efforts in the real world are much moreorganized, which may explain the lack of effects observed in experiment Cases 3-1 and 3-3. Consequently,
we intend to add more complex agent behavior in future versions of our model.
Finally, in the current model, buildings and obstructions do not have direct effects on the voice
intelligibility, and the model for the sound wave propagation is fixed. It would be interesting to further
investigate the sound propagation physics model by adding the effect of buildings and visibility tomessage transmission, and also the effect of sound wave speed on the simulation.
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