Cole Davis and Ryan Emptage
Biocomplexity in Escape Panic
In situations such as mass panic in a crowd, every individual follows a general set of
rules to hopefully ensure their personal survival. These tactics include rushing, pushing,
climbing, or whatever is required to leave a dangerous area. These types of panics become a
type of fractal, as panic sets in on each individual and causes them to obey a few fundamental
rules. In crowded places such as nightclubs or soccer stadiums, any time of emergency is likely
to induce panic. Many unnecessary deaths have occurred due to the onset of panic. As stadiums
and buildings grow in size, so do crowds inside them. This means that more deaths will occur if
an emergency is followed by panic. What can be done to curb this disturbing trend?
European scientists have used computer modeling to simulate panic triggered crowd
stampedes. Crowd stampedes occurring in highly congested areas like stadiums and theatres can
prove fatal. Helbing, Farkas, and Vicsek used a computer model to quantitatively explain the
human behavior of panicking individuals. After observing videos of escape panics, they were
able to delineate several generalizations that needed to be accounted for in their model. Among
the generalizations were that people move or try to move at a quick pace, interactions become
physical in nature as people start shoving, moving and passing of a bottleneck becomes chaotic,
arching and clogging occurs at exits, dangerous pressures are applied to individuals, fallen or
injured people further decreases the escape velocity by becoming obsacles, panicking people
tend to do what others do (mass behavior), and alternative exits are often ignored.
To describe these phenomena they created a generalized force model using Newton’s
formula force equals mass multiplied by acceleration. In this case, acceleration is equal to the
individuals desired speed multiplied by the direction minus the actual speed all divided by time.
This makes sense because acceleration is the change of velocity with respect to time. They
incorporated individuals’ tendencies to avoid contact (when possible). When individuals do
touch, two additional forces are added; a body force and a sliding friction force. Escape panics
are highly dangerous and sometimes fatal events, removing any possibilities of actual
experiments. Helbing, Farkas, and Vicsek were forced to model their theories and experiment
using computer simulations. To further simplify the model several assumptions were made. All
individuals were denoted a mass of 80 kilograms. Individuals could move at speeds beyond 5
m/s. Acceleration took place over half a second for each person. The shoulder widths (radii) of
the individuals varied between .5 and .7 meters. This method would produce an accurate and
measurable model with some (but still limited) variability in the system.
In the group’s first simulation, they modeled a crowd of people hurrying out of a large
room with one small exit. This model would be consisted with studies of night clubs and other
crowded rooms with few exits. In the model, as the people rushed to the door, they created a
semicircle around the exit, all attempting to squeeze through. The result was an arching effect in
which the forces of the walls on the outermost members of the arch, and create packing around
the exit. When the velocities of the people were around 1.5 m/s, the leaving time for the people
was minimal. If people devolved into a state of panic and the individual velocities increased, the
arching effect was magnified, preventing people from leaving the room and injuring some who
could not stand the intense pressures of the interpersonal forces generated by this mass of
panicked individuals pushing on each other. These fallen individuals further hindered the
progress of the mass exit, and in case of fire, would have cost more lives than necessary.
Helbing, Farkas, and Vicsek found that placing a column directly in front of an exit reduced
interpersonal pressures caused by the pushing mass and allowed more people to escape the room
uninjured.
In a second simulation, the research team studied a mass of people moving down a
corridor. They found in the models that if a widened section was added to these corridors, the
number of people who successfully passed through the corridor per unit time was diminished.
Helbing, Farkas, and Vicsek constructed their model so that the individuals would tend to spread
out in the wider sections due to their portrayal of interpersonal forces and friction. When the
corridor narrowed again, a bottleneck was created and caused decreased efficiency of the system.
In the third simulation, Helbing, Farkas, and Vicsek studied a crowd in a smoky room,
placing two exits at opposite ends of the room. When the individuals attempted to find the exits
randomly for themselves, most escaped the room. When the individuals adopted herding tactics
and followed one or two “leaders”, the crowd conglomerated in one location, and effectively
trapped the leader within the herd. When the group combined these two models into a situation
in which individuals formed small groups and each small group independently searched for an
exit, the time it took for all of the individual to leave was reduced significantly. The research
team concluded that in a situation such as a smoky room, a combination of herding and
individualistic behaviors was favorable.
This particular model of panic and escape tactics is ingenious and useful, but there are
numerous components of his models which could be changed to increase the accuracy of the
simulation. First, the simulation would have been much more accurate if it had incorporated
three-dimensional motion. In actual escape panics, some individuals are trampled on and
stepped over. In this group’s simulation, those injured people would become simple roadblocks.
In order to incorporate a three-dimensional simulation into two, I believe that Helbing, Farkas,
and Vicsek should have allowed their individuals to step over the injured by having them move
through the injured at a slower speed. This would have created a pseudo-three-dimensional
model of panic. Secondly, parameters like speed and forces created by each person should have
been individualized. In the simulation, the only individualized parameter was the radius of the
circle which modeled each individual. The parameters of velocity, individual force, and mass
were kept constant. If the team personalized their parameters for each individual, his simulation
would have been more accurate. Third, Helbing, Farkas, and Vicsek could have incorporated
communication between individuals and therefore changes in strategy. In his “herding”
simulation, crowds of people would pack themselves into a wall, unknowing that the “leader”
desires to move in the opposite direction. If communication was included, the leader would let
the individuals around him or her know that he or she was at a wall and needed to change
direction. The nearby individuals would accommodate this, and the leader would not be pinned
powerless against a wall. Each one of these critiques would create a more accurate simulation
since these variables affect panic in the real world.
Helbing, Farkas, and Vicsek’s examination of panic, normally studied in the social
sciences, shows much promise when studied from the perspective of the applied sciences and
biocomplexity. Mathematical and computational modeling is at the cutting-edge of the sciences.
The inter-disciplinary approach is going to have an important role in future research.
Biocomplexity studies will be responsible for preventing panics, and saving lives.
Bibliography
Farkas, Illes; Helbing, Dirk; and Vicsek, Tamas. “Simulating Dynamical Features of Escape
Panic.” Nature. 2000: pg 487-490.
Vicsek, Tamas. “Panic: A Quantitative Analysis.” http://angel.elte.hu/%7Evicsek/.