Multi-Agent Simulation of Civilian’s Evacuation
Yutaka Ishimoto, Hiroyuki Kobayashi, Shigehiro Hashimoto
Department of Biomedical Engineering, Osaka Institute of Technology,
Osaka, 535-8585, Japanhashimoto@bme.oit.ac.jp http://www.oit.ac.jp/bme/

ABSTRACT
Civilian’s evacuation has been simulated with amulti-agent system to propose an evacuation
plan in a megalopolis.
In the simulation,
floating-point number coordinates are employed
to minimize calculation errors. A megalopolis
is comparted into small areas to be
distributively computed by numerous computers.
Behavior of human in evacuation ischaracterized as “an agent”, which is a robot
program that acts autonomously. A global city
structure is modeled as topography data of a"map". The topography data, which consist of"nodes" and "edges", are input into virtual space
on a computer in real number coordinates.
Nodes and edges construct routes for escape,which correspond to a crossing of a road and to astraight-line road between nodes, respectively.
The agents evacuate with circumventing
obstacles and another agent in human-likebehavior on the designed simulation primitive,
which is a unit of distributed processing. The
multi-agent simulation system might beeffective to simulate civilian’s evacuation in amegalopolis.
Keywords: Biomedical Engineering, Simulation,Multi-Agent
Simulation,
Evacuation,
Distributed Processing, Floating-Point Number
Coordinates, Computer Clustering
1. INTORDUCTION
Natural disasters occurred frequently all over
the world recently.
Civilian’s smoothevacuation is one of the most important themes
to be studied to minimize damage in themegalopolis. Evacuation is not easy in thecomplicated structures of megalopolis, whichcauses a panic and secondary disasters incivilians.
Although various simulations have been
proposed on evacuation, some problems remain
to be solved [1, 2]. Because of the great mass ofdata and complicated structures of megalopolis
on evacuation, employing integer coordinatescauses accumulated error, and mass ofcalculation exceeds capability of a computer.
In the present simulation, floating-point
number coordinates have been employed tominimize calculation errors, and humanevacuation has been simulated with agents in asimulation primitive.
2. ARCHITECTURE OF SIMULATOR
Multi-Agent Simulation System
Behavior of human in evacuation ischaracterized as “an agent”, which is a robot
program that acts autonomously.
Anappropriately designed agent makes thesimulation system reliable.
Virtual SpaceAn agent moves in the virtual space. A global
city structure is modeled as topography data ofa "map". The topography data, which consist of"nodes" and "edges", are input into virtual space
on a computer in real number coordinates.
Nodes and edges construct routes for escape,which correspond to a crossing of a road and to astraight-line road between nodes, respectively(Fig. 1).
Each node has a node ID(identification) number relating to a coordinate,and edge has width.
AgentPath
Node

Simulation primitive(a) Map(b) Simulation primitiveFig. 1: Virtual space.

Map

Simulation primitiveFig. 2: Simulation primitive.

Simulation Primitive
A “simulation primitive” corresponds to aprocessing unit in distributed processing. The
simulation primitive is converted from the map
data. Agents move in the simulation primitive.
The map data have width and a coordinate.
The simulation primitive of polygon is madewith these data. The simulation primitive isdefined in the area from the middle of a node to
the middle of an adjacent node (Fig. 2). The
simulation primitive of polygon consists of an ID
number, apical coordinates of the polygon,agents in the polygon, and ID numbers of theadjacentsimulation
primitives.
The
simulation primitive constructs virtual space
like mosaics (Fig. 3).
When the number of agent increased, allsimulation primitives are not handled with one
computer, but with multiple computers (Fig. 4).
Agent
Each agent has an ID number, speed, size,
coordinates in a simulation primitive,
temporary objective coordinates in a simulation
primitive and an ID number of an objective
simulation primitive.

NodePath

The ID number of agent belongs to the agent
itself and does not change during simulation.
The system of coordinates in a simulation
primitive is the x-y coordinates at the location of
the agent. The temporary objective coordinate(a) Map

Simulation primitiveagent(b) Communication among
primitives

simulation

Fig. 3: Simulation primitive constructs
virtual space.

Fig. 4: Computer clustering for DistributedProcessing.

in a simulation primitive is the coordinate
nearest to the objective adjacent simulation
primitive. The agent moves in a local area, andcircumvents an obstacle. The agent chooses
the next point to move for evacuation, andmoves to the appointed coordinate, when there
is no obstruction. The agent circumvents anobstacle,
whentheagent
encountersobstructions: another agent or an obstacle.
Algorithm to Circumvent Obstacle
Define the coordinates ( x, y ) , where an agent isseated, when the central coordinates of theobstacle on the way of the agent’s moving
direction are ( x0 , y 0 ) .

dx = x − x0 , dy = y − y 0( x + dy, y − dx) . These coordinates are locatedin a tangential direction against the obstacle,
which enables the agent to circumvent theobstacle. There is a possibility for two agentsto choose the same direction to move, when two
agents encounter each other. The agent choose
the moving direction at random to avoid twoagents moving to the same direction, crossing
each other’s path, falling into “deadlock” (Fig.
5).

obstacle

Temporary
destination

Center of obstacle
Direction of destination

AgentA simulation was performed, whether an agent
could avoid an obstacle and another agent, andadvance to the refuge direction in the simulator
developed.
Ten agents evacuate from the left side to theright side, and the other ten agents evacuatefrom the right side to the left side; where eachagent moves at the speed of 0.4 m/s, and has aradius of 1 m in the simulation (Fig. 6).

(1)The agent moves to the point nearer to thedestination, after calculation of the coordinates( x − dy, y + dx)
andof
twopoints:

Agent moves totangential direction
against obstakle

TestA simulator was developed in the environmentshown in Tables 1 & 2.

Route of agent

Choose nearer routeFig. 5: Algorithm to circumvent obstacle.

Table 1: Development environment of main
simulator
Development language C++
Software
GNU C++ Compiler
Table2:Development
environmentofsimulation viewer
Development language Java
SoftwareJava Development Kithandled with a current computer [1, 2].
AgentPath

4. CONCLUSION

Obstacle

Fig. 6: Simulation to circumvent obstacle.

3. RESULTS AND DISCUSSION
The results of the simulation show that every
agent circumvents obstacles smoothly without
deadlock like a human being, and that every
agent moves to the evacuation direction (Fig.7).
The simulator with the floating-point number
coordinates successfully simulated evacuation ofhuman with agents in the simulation primitive.
The algorithm for an agent to circumvent anobstacle is confirmed in the simulation.
In the present simulation, floating-pointnumber coordinates have been employed tominimize calculation errors. The global
simulation has been divided into comparted
simulations for distributed computing underclustering computers, because approximately
500,000 is the limit number for agents to be

Fig. 7: Agents are circumventing obstacle.

In the present study, the agents evacuate withcircumventing obstacles and another agent in
human-like behavior on the designed simulation
primitive, which is a unit of distributedprocessing.
The multi-agent simulation
system might be effective to simulate civilian’sevacuation in a megalopolis.
REFERENCES[1] M. Fujioka, K. Ishibashi, “Multi Agent
Simulation Model for Evaluating Evacuation
Management System against Tsunami
Disaster”, Journal of Architecture, Planning

andEnvironmentalEngineering,Transactions of AIJ. No. 562, 2002, pp.

231-236.
[2] M. Fujioka, K. Ishibashi, H. Kaji, K.
Tsukagoshi, M. Fujioka, “The Information
Dependability for Evacuee on Multi-Agent
Based Simulation Model”, 4th Workshop on
Agent-Based Simulation, 2003, pp. 64-69.