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

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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. 4
DEFENCE SCIENCE AUSTRALIA The modelling includes details as small as individual trees and shrubs, the presence or absence of which can have significant effects on pathways taken, and thus, simulation outcomes. This approach to terrain modelling more accurately represents the real-world conditions in which many large-scale human movements occur. In the demonstration models developed by DSTO to date, one of the most advanced environments is a rural environment with a country town threatened by an approaching bushfire front. Another is a model of a city environment facing a fire threat. The initial conditions for these can be set in various ways to study different problems. Outcomes are delivered both in numerical form as probabilities that particular eventualities will occur, and as threedimensional (3-D) animations of the event that can be observed on screen. The outcomes given include not only the direction of travel of the crowd but also the mood prevalent, and any changes arising. Verifying the modelling will be carried out by running simulations of a readily observable real-world situation, such as rush-hour crowd flows in a city setting, and studying the outcomes compared to data and video footage obtained from actual events. Initial work has shown the models to produce true-to-life outcomes. Developmental challenges Despite the seemingly advanced state of the work, the researchers are in fact still defining the problems associated with producing this capability. So far, various approaches have been devised and then tested for limitations – ‘looking for things to break’, as Dr Reid puts it – to see where further development is required. Drs Reid and Ivancevic are confident, meanwhile, that the basic framework of approach adopted is viable for the modelling capability they want to deliver. With some simulations currently taking several days to run before outcomes can be observed, investigations are also underway to optimise the use of available computing resources via parallel and distributed computing techniques. A long-term aim is to harness computing resources sufficient for running simulation exercises in real-time, allowing changes in conditions to be introduced during the simulation and the effects observed immediately. Plans for further work include a number of modelling refinements, such as the ability to model the influence of injuries and explosions as well as weather on mood, and the behaviour of agents. The effect agents can have on crowd behaviour involves analysis in terms of three groupings, designated ‘blue’ for those who intervene to stabilise events, ‘red’ for those who act as destabilisers, and ‘whites’ for those who begin as neutral participants. The researchers expect they could be ready to undertake subsequent clientrelevant work in about a year’s time. Above: Dr Darryn Reid (rear) and Dr Vladimir Ivancevic with crowd behaviour modelling software. Above left: Frames taken from a DSTO crowd behaviour modelling animation. 5
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