This document describes a crowd behavior simulation of a fire outbreak inside a cinema. Each individual agent decides their own escape pathway based on local perceptions. Agents that do not interact with others escape more efficiently than interactive agents. The model dynamically simulates different crowd behaviors in emergent situations, useful for survival guide design.

1. A Survival Guide during Fire Outbreak inside a Cinema
Hsin-Huei Cheng
Instructor : Demetri Terzopoulos
University of California at Los Angeles
Abstract
A crowd behavior simulation is performed in a situation where fire outbreak occurs inside a cinema. Each
individual agent inside the simulation will decide their own escape pathways based on their local
perception of the environments (i.e., agents and obstacles). It is found that agents who do not interact with
their local environment (i.e. people that are stayed clam) will escape more efficiently than those who
interact with other agents. This model allows for the dynamic simulation of different crowd behaviors in
emergent situations, which will be useful for survival guide design.
1. Introduction
“Oh My God! There’s a fire!”. Imagine when you are This paper is organized as follow. The design of a
inside a cinema watching a famous movie, but virtual environment model is discussed in Section 2.
suddenly the fire alarm rings, what would you do? In addition, the details of the autonomous model and
Would you cry, run or wait to get rescued? Everyone the different variations will be presented in Section 3.
behaves differently in facing an emergency situation. The implementation methods will be discussed in
People’s reactions ultimately influence the overall Section 4, followed by results and discussions in
human survival rate in the incident. In the occasion of Section 5. Finally, the conclusions and the future work
fire outbreak in an enclosed area, people were found will be provided in Section 6.
killed by stepping on each other, instead of being killed
by the fire. Many human and environmental factors are 2. Virtual Environmental Model
coupled together that will affect the final outcomes.
The most critical factor is the personality of In real life, people interact with local their
individuals. Other factors like the environment, the environments in deciding the escaping pathway.
physical health situation, the time of making decision, Similarly, in the simulation, each agent interacts with
and crowd’s behavior etc. also play important roles. their local environment in order to decide the best
escaping route. Therefore, the design of the virtual
In this project, I simulate the real time dynamics of environment is crucial for the correct implementation
people movement inside a cinema in response to a fire of the simulation.
alarm. Each person is an autonomous agent. They can
interact with each others, as well as the static obstacles To begin with, the virtual cinema environment will
inside the cinema. The agents make decision for their be briefly introduced. The plain view of the cinema
local pathways, and this information is collected in a layout is shown in Figure 1. As shown in the figure,
central decision maker at each time step to implement the grids in pink color represent the exits. Each agent
the agents’ motion. In particular, each agent was selects the nearest exit at each time step, which is
randomly assigned with different properties in order to determined by the Euclidean distance between the exit
simulate different crowd behaviors. and the agent. The seats are illustrated in blue girds.
These seats are stationary obstacles, where agents can

2. only go around the obstacles instead of walking across 3.1 Autonomous Model
them.
The agents were represented by circular-shaped
The virtual environment model consists of a objects in the simulation. Agents with different
stationary map, a mobile map, and a path map (Figure properties are represented with different colors. The
2). The stationary map records the grid locations properties of the agents are summarized as follow:
where the obstacles (i.e. seats) occupy. This map is
stationary with respect to time. For dynamic moving - Physical factors: different walking speeds
agents, the mobile map stores the information for represent different physical conditions
occupied cells in real time. Agents that are within - Mental factors: selfishness and helpfulness
specific cell are recorded in the “occupied agent list”. - Group factor: belongs to a group or alone
The dynamic map is updated at each time step. Both
stationary and mobile maps are necessary input Each agent possesses three different states. The initial
information for the central decision maker to state is indicated as “sitting” state. When the agents
implement the paths. The implementation will be are alerted by the fire alarm, their states change from
stored in the path map. the “sitting” state to the “running” state. After a
defined time period, if the agents are trapped inside
the cinema, then their states will turn into the “dead”
state, which implies that the agents are killed by the
fire. For those who have successfully reached the
exits, their states will turn into “alive” states. A
summary of state changes is illustrated in Figure 3.
Figure 1. The plain view of cinema.
Figure 3. State change of an agent
3.2 Autonomous Behavior Model
The general behavior of agents can be classified as
below (Figure 4).
A. helpful agents meet helpful agents: the slower
agent has the priority to move to the next grid
B. selfish agents meet helpful agents: selfish
Figure 2. Upper left: stationary map; upper right: ones have the priority to move to the next grid
mobile map; lower: path map C. selfish agents meet selfish agents: one of them
will be given the priority to move
D. group members meet other individuals: group
members have the higher priority if the group
3. Autonomous Agent Behavior is already in the process of moving. Group
members cannot be separated.
The dynamic agents are the major character in the
simulation. Here, I will first introduce the properties
of the agents and then describe the behavior models
based on these properties.

3. is the next moving position of each agent at next time
step.
Since all the agents movements have to be
coordinated at the same time, a path decision maker is
required to coordinate the movement. This is achieved
(a) (b) (c) (d) by the function “confirmPath”. For each grid on the
path map, “confirmPath” checks the occupied status.
Figure 4. (a) H1 has lower moving speed so it has the There are some complicated rules for the
priority to move. (b) S1 has the priority to move. (c) implementation, which are outlined as follow:
Either S1 or S2 has the priority. (d) G1 has the priority
to move if one of the group members (e.g. G0) first A. A grid can only be occupied by one agent p
passed by the grid and “confirmPath” approves p’s next moving
position if
- p does not belong to any group
3.3 Sensing Area - p belongs to a moving group and the previous
member just walked through the grid
In reality, people can only sense their local - p belongs to a group and is the leader of the
environment to make a decision. In the virtual group
environment, each agent is provided with the ability to
sense its local neighborhood, in order to decide the B. in the situation where the grid is going to be
best moving direction. For instance, if the number of occupied by more than one agent
neighbors of an agent is less than that of its right hand - compare each candidate’s properties and
side, the agent will choose the direction where there follow the rules described in Section 4.2 to
are fewer neighbors (i.e. higher chance to escape find the “winner” who can occupy the
faster). With this concept of neighborhood sensing, grid at the next time step. The agents who are
each agent updates a sensing list which records the “lost” in the comparison will remain at the
occupied cells within a specified area of interest at same position.
each time step. An example of the sensing area is
shown in Figure 5.
Following the method, agents can progressively reach
the nearest goal (i.e., the exit).
Figure 5. The 2 tier sensing area of agent p. The light
blue area represents the 1st tier, whereas the dark blue
region represents the 2nd tier.
Figure 6. The pseudo codes of the method.
4. Method Description
An outline of the methodology used for computing a 5. Results and Discussion
path for an agent is provided in Figure 6. The function
“predictNext” is to find the optimal intermediate In this section, I will illustrate some agent behaviors
positions (i.e. sub-goals) toward the final goal. The based on the behavior model mentioned in Section
function refers to the stationary map, as well as the 2- 4.2. These demonstrations are displayed on the
tier sensing list to find the next direction that has a websites at
higher escape probability. The output of this function http://web.me.com/stella0107/stella/CS275_AL.html.

4. Different crowd behaviors were simulated based on Acknowledgements
different agents’ properties and some interesting
phenomena were observed. For instance, Demo 3 and I would like to thank Prof. Terzopoulos’s lectures that
4 demonstrate a situation where agents located on the let me understand the beauty of Artificial Life. I
left hand side of the cinema do not interact with other would also like to thank the instructor Prof.
agents, therefore they escape to the exit based on the Terzopoulos, as well as all the people who were
shortest pathway; while agents located on the right involved in the development of the simulator for
side are programmed to interact with other agents, CS174/274C. Special thanks to Professor Faloutsos
therefore a longer decision making process is required and the teaching assistant Gabriele Nataneli.
for individual agent to execute their pathway. This is
reflected from the simulated behavior model, where
some agents become hesitate to decide which way to References
go. They interact with the dynamic environment in
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faster. However, this leaded to a situation where some behavior," X. Tu, D. Terzopoulos, Proc. ACM
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6. Conclusion and Future Work Terzopoulos, Communications of the ACM,
42(8), August, 1999, 32–42.
Crowd behavior simulation is a very interesting topic,
especially when the simulation is coupled with the [5] “Steering Behaviors For Autonomous
complex personalities of individual agents. In this Characters”, Craig W. Reynolds, Sony Computer
project, I have demonstrated a crowd behavior model Entertainment America.
based on individual agents’ properties, where some
interesting observations were found. Changing the
behavior models will lead to different outcomes that
may be outside our imagination. The results presented
in this report are instructive to educate people how to
escape efficiently from an emergency situation, such
as fire outbreak inside a cinema.
Future work includes the optimization of the data
structures, as well as the behavior models. The project
is a small-scale simulation model with a few agents at
the moment. The ultimate goal is to design a large-
scale simulation that can realistically simulate the real
life situation.