DSA Volume 1 Issue 4 December 2010 - Defence Science and ...

Crowd behaviour analysis to
understand the milling monster
DSTO is developing crowd
behaviour modelling that
improves on the complexity
of situations simulated, and
also provides a capability for
making predictions of crowd
motions, thus allowing for
more effective intervention
measures overall.
The work began as ‘blue sky’ research
initiated in DSTO by Dr Darryn Reid. His
interest in crowd behaviour was inspired
by observations of actual events, such
as pilgrimage processions and football
matches, having noted that crowds
now feature increasingly prominently
in Army operational environments.
“Crowds assembled for some common
purpose or interest may appear to flow in
a stable orderly manner, but after some
seemingly trivial change or interruption, as
happens when a person stumbles or turns in
the opposite direction, chaos of disastrous
proportions can ensue with widespread
injuries and loss of life,” he says.
Concern
The way in which crowds move is thus of
central concern to military commands as
well as civilian authorities and disaster
relief agencies. Some scenarios of interest
include civilians passing through a combat
zone to escape conflict, and persons
fleeing natural disasters such as tsunamis,
earthquake and fire – the latter being of
particular significance within Australia.
The need for insightful analysis to inform
incident prevention and management
becomes even more apparent with the
knowledge that some outcomes are counterintuitive.
In one such example, a column
put inside the doorway of a building,
partially obstructing movement, can actually
smooth crowd flows. Another example is
that the use of roadblocks to control traffic
flows during a bushfire emergency can
make flow conditions more unstable.
After looking at existing crowd behaviour
models, Dr Reid found they offered
somewhat limited capabilities. Firstly,
they were only able generally to simulate
crowd behaviour in simple confined
spaces, such as a stadium or building
interior. Secondly, the simulations were
descriptive only, offering no understanding
as to why behaviour may change, and
with no ability to predict this change.
Teaming up with fellow DSTO mathematician
Dr Vladimir Ivancevic, he set about
investigating ways of improving on the
simulation capabilities available.
New way of modelling
crowd behaviour
A basic problem identified by the DSTO
researchers with previous models was
that they derived states for crowd
behaviour by calculating the state of
each individual component and adding
all of these to produce a sum effect – a
‘bottom-up’ form of approach.
The DSTO view of things is somewhat more
complex. “The individual has an effect on
the crowd and the crowd also influences the
behaviour of the individual, so there is an
interactive process involved,” says Dr Reid.
“This is why crowd movement is
chaotic, which is to say, the outcome is
exquisitely sensitive to small changes
in conditions and earlier events.”
His analysis proposes a system with
three levels in play simultaneously;
the individual, the overall crowd,
and a meso level in between where
aggregate motions are formulated.
“Only by representing what’s going on at
these three levels simultaneously can we
expect to characterise the different overall
chaotic motions and sudden changes of
motion – called phase transitions – that
real crowds can display,” says Dr Reid.
Entropy and crowd behaviour
Another major difference to previous
modelling is the use of a theoretic
framework based on entropic geometry,
entropy being a measure of the level
of disorder that exists in a system.
Out of this has come an approach
Dr Reid terms ‘behavioural composition’
that draws together previous
approaches in a single framework.
“Whereas other models see either the
whole emerging from its parts, or the
whole being reduced to its parts, our
approach attempts to unify both notions.
“Simulating crowd behaviour processes
in this way requires solving what
mathematicians refer to as ‘large coupled
systems of nonlinear Schrödinger equations’.
“For this purpose, we therefore need not only
vast amounts of computing power, but also
algorithms that can accurately solve these
large and complex systems of equations
without the results becoming effectively lost
in numerical noise – and both of these needs
pose major practical problems,” he says.
A further significant aspect of DSTO’s
modelling approach, the use of quantum
probability theory, deftly solves an intractable
problem facing previous approaches and
also gives it a predictive capability.
In previous approaches, modelling of
individual behaviours with high accuracy
was required in order to arrive at an
accurate aggregate crowd model, which is
extremely difficult. This difficulty can be
avoided using a probability theory approach
because, even though the motions of
individuals cannot be predicted with any
accuracy, those of the crowd overall can.
The way is then made open to not only
describe the chaotic motions of a crowd at
any time but to also generate predictions of
likely motions for a given set of preconditions.
More realistic environments
for scenario studies
In addition to the advances being made
in modelling crowd behaviour processes,
the research has arrived at modelling
capable of simulating crowd events in
more complex environments. These
involve a mix of terrain types, rural and
urbanised, some parts freely traversable
and some more constricted, with crowd
flows taking multiple paths through them.
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