The Behavior of Crowds
Two scientists at the Max Planck Institute in Berlin have been studying behavior influenced by probabilities, and their findings are particularly interesting when it comes to the dynamics of crowds.
In 1995 Mr. [Dirk] Helbing and Peter Molnar, both physicists, came up with a “social force” computer model that used insights from the way that particles in fluids and gases behave to describe pedestrian movement. The model assumed that people are attracted by some things, such as the destination they are heading for, and repelled by others, such as another pedestrian in their path. It proved its worth by predicting several self-organizing effects among crowds that are visible in real life.
One is the propensity of dense crowds spontaneously to break into lanes that allow people to move more efficiently in opposing directions. Individuals do not have to negotiate their way through a series of encounters with oncoming people; they can just follow the person in front. That works better than trying to overtake. Research by Mr. Moussaid suggests that the effect of one person trying to walk faster than the people around them in a dense crowd is to force an opposing lane of pedestrians to split in two, which has the effect of breaking up the lane next door, and so on. Everyone moves slower as a result.
Another self-organizing behavior comes when opposing flows of people meet at a single intersection: think of parents trying to shepherd their children into school as other parents, their sprogs already dropped off, try to leave. As people stream through in one direction, the pressure on their side of the intersection drops. That gives those waiting on the other side more opportunity to go through, until pressure on their side is relieved. The result is a series of alternating bursts of traffic through the gates.
This oscillation in flows is clever enough to have got Mr. Helbing wondering about its application to cars. Traffic-light systems currently operate on fixed cycles, with lights staying green on the basis of past traffic patterns. If those patterns are not repeated, drivers are left to idle their engines for too long at red signals, raising emissions and tempers. Mr. Helbing thinks it is better to have decentraliezd, local systems, which — like parents at the school gates — can respond to a build-up of traffic and keep the lights on green for longer if need be. City authorities agree: Mr. Helbing’s ideas will soon be implemented in Dresden and Zurich.
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Another problem with assuming people act like particles is that up to 70% of people in a crowd are actually in groups. That matters, as anyone trying to get past shuffling tourists knows. It also leads to some lovely fine-scale choreography when small groups are squeezed. Observations of pavement crowds in Toulouse in France show that clusters of three and four people naturally organize themselves into concave “V” and “U” shapes, with middle members falling back slightly. If a group of three people cared about moving quickly, they would behave like geese and form a convex “V”, with the middle member slightly in front to forge a path. Instead, they adopt a formation that enables them to keep communicating with each other; talking trumps walking.
The physics-based models do have an answer to this problem of “arching” (so called for the shape of the crowd that builds up around the exit). Their simulations suggest the flow of pedestrians through a narrow doorway can be smoothed by plonking an obstacle such as a pillar just in front of the exit. In theory, that should have the effect of splitting people into more efficient lanes. In practice, however, the idea of putting a barrier in front of an emergency exit is too counter-intuitive for planners to have tried.