Disclaimer: This is a “toy” simulation, based on
untested assumptions. It should not in any way be used in making
real-life planning decisions.
Created with NetLogo;
View/download model file: vevac.nlogo
This model simulates "vertical
evacuation," a technique for bringing people to safety by having them
"go up" in buildings, for instance, in flooding or tsunami situations.
The model implements two behaviors: seeking to go up (modeling people who
receive and understand the warning, know what to do and do it), and seeking to
get outside (modeling people who want to get out for whatever reason).
The project demonstrates how individual
behaviors may slow down overall movement, create traffic jams, and block access
to safe zones.
Click on the SETUP button to set up the space and generate a set
of people in the world.
The gray horizontal strip represents the
street level, and the vertical white strips represent buildings (stairwells in
buildings to be exact).
Solid arrows represent "uppers" (people
seeking higher altitudes), hollow arrows represent "outers" (people
seeking to reach ground level). The direction of the arrow shows the individual's
direction of movement. The color of the arrow indicates the safety status of
the individual (green being safe, yellow at rist and
red unsafe), with respect to altitude.
Click on the GO button to start the
simulation.
The plot on the left shows the number of
people in each safety zone, at any given time.
All individuals will move forward until they
are blocked by someone else, or they need to turn (e.g., to go up or get onto
the street). All individuals slow down and speed up as the path ahead of them
is blocked or clears. Each individual has a different maximum speed at which
they can move.
If an individual is blocked, they will slow
down or, if possible, "step around" the block. Individuals have a
preference for stepping to the left if they are passing someone, but will
choose to step to the right in some cases. They have a similar preference to
step to the right if they are yielding to someone heading in the opposite
direction. Note that, while they can step around one other individual, they are
not intelligent enough to "go around" a group of people.
If they are blocked for too long, they will
turn around and try in the opposite direction. Note that the street
"wraps" around the world, so that they will move off one side of the
view to reappear on the other.
An "upper" on the street will move
forward until they are opposite a building, then turn and try to enter the
building. They are not smart enough to head for the nearest building. Once they
are in the building, they will not give up on trying to go up.
An "outer" in a building will not
give up on moving downward until they are on the street. Once on the street,
they will keep moving in a randomly chosen direction until blocked.
The model will stop either when no one is
moving, or when the only individuals moving are outers on the street.
In addition to modeling simple movement and
isolated decision-making, the model also demonstrates socially transimitted behavior. "Directors" are people who
try to convince others to emulate their behavior, i.e., if they are
"uppers," they will tell people to go up, and if they are
"outers," they will tell people to get out. If a non-director is
convinced by a director, they will change their behavior (if necessary) to match
that of the director.
Directors can be heard only by their
immediate neighors. Each director has a
"credibility" factor, and each non-director has a "belief"
factor, so that some directors are more convincing than others and some
non-directors are more difficult to convince than others.
In the present simulation, "upper"
directors are generally more convincing than "outer" directors.
Directors are identified by a different
arrow shape. Individuals who have been converted are indicated by a heavier
outline.
The right-hand plot shows the percentage of
the population that exhibits the "upper" behavior.
SAFE-HEIGHT controls the altitude that is
considered safe (where individuals turn green).
STAIR-WIDTH controls the width of the
buildings.
STREET-WIDTH controls the width of the
street.
NUMBER-OF-BLOCKS controls the number of
randomly placed obstacles in the street. These are represented as black
squares.
NUMBER-OF-PEOPLE controls the total number
of individuals in the world.
PERCENT-UPPERS controls the percentage of
"uppers." The rest will seek to get outside.
DIRECTORS controls whether the director
behavior is included in the simulation. Note that the number of directors is
fixed at approximately 10% of the total population, selected
at random from the population.
Note how clots will form at building
entrances and the middle of the building is often unoccupied.
Note how a phalange of uppers or outers can
block anyone from moving in a building. The narrower the building is, the more
likely this is to happen.
Note how directors convert others, and how
the rate of conversion increases if a director is stuck in a traffic jam..
Note how the higher credibility of
"upper" directors tends to make the number of "uppers" increase,
but then, as the "upper" directors reach their own safe zones, the
remaining "outer" directors may make it decrease.
Set the SAFE-HEIGHT to 0 and mentally
"reverse" the meaning of the red and green colors. You are now
looking at a building evacuation model, e.g., for a fire or an earthquake
situation. Try setting the PERCENT-UPPERS to 0.
Set STAIR-WIDTH to 1. You now have the
beginnings of a queuing model, e.g., an evacuation situation where individuals
must pass through a checkpoint.
Change the behavior of individuals so that they "see" the nearest building (maybe allow the user to specify how close they have to be to "see" something).
Change some of the directors so that they do
not evacuate themselves, but stay in place (possibly at key locations such as
building entrances) to direct traffic. How would that change evacuation
results?
The BehavoirSpace
included in the model defines five experiments, that vary only in the
percent-uppers and whether directors are on or off. Model parameters for the
five conditions are shown in Table1.
safe-height |
5 |
|
stair-width |
4 |
|
street-width |
5 |
|
number-of-people |
100 |
|
number-of-blocks |
0 |
|
|
|
|
Exp |
percent-uppers |
directors |
100 |
100 |
off |
50 |
50 |
off |
50-dir |
50 |
on |
33 |
33 |
off |
33-dir |
33 |
on |
Table 1 Model settings for each condition.
Tables 2 and 3 show a set of results from running the experiments (running each condition five times), showing
the average time until evacuation is “complete,” the number of
people who reach safe zones, and the standard deviations for each of these
measures. Note that the evacuation is considered “complete” when
the number of people reaching safe zones has slowed to a trickle for a
considerable length of time. That doesn’t mean that everyone who could be
evacuated has been or that a large group might not evacuate later, just that
the wait is too long to consider them evacuated safely.
The five experimental
results are contrasted with an “optimal” condition that is based on
pure calculation. It assumes that all individuals start at the midpoint between
the two buildings, head directly toward the nearest building and go up. The
calculation assumes that all individuals move at a constant speed (half as fast
as the median of that used in the simulation), that individuals are launched
in groups of four (two toward each building) at each time step. In other words,
this is the fastest that people could possibly be
assumed to evacuate.
Exp |
Avg Time |
Avg Safe |
StD Time |
StD Safe |
100 |
292.2 |
72 |
27.65 |
0 |
50 |
814.4 |
42.6 |
171.31 |
8.38 |
50-dir |
789.2 |
55.6 |
157.82 |
8.65 |
33 |
821 |
27.8 |
273.37 |
5.07 |
33-dir |
724 |
35.6 |
123.08 |
10.11 |
Optimal |
124 |
72 |
0 |
0 |
Table 2 Results over five runs of each condition.
Table 3 Results over five runs of each
condition.
If these results were confirmed by statistically valid
experimentation (five runs is insufficient to achieve meaningful confidence
levels, and further experimentation with varying population sizes is necessary),
and the model were based on realistic assumptions, the following
conclusions could be made:
Note, again, that this model is not based on tested assumptions. Real-life
planning and decision-making should not be based on these results or
their interpretation.
Version 1.0
2007/02/07
This model was developed at the Pacific Disaster
Center, Kihei Maui HI, by NRC Research Associate Susanne Jul, PhD, to
illustrate the potential for incorporating agent-based modeling in disaster
management.
Copyright 2007 SJul. Rights to
non-commercial use and development granted, with appropriate attribution.