Movement within crowds

The above video is from a software package to model collision avoidance within crowds. It uses an algorithm that calculates the movement vectors of nearby individuals to allow an individual to determine the best path to avoid walking into another person. All of the individuals are making those calculations simultaneously, so their paths are continually being updated as they maneuver through the crowd.

While good for CGI animations, and for gross modeling of crowd behavior, real humans have a much more complex decision-making process. For example, Westerners will instinctively turn to the right to pass an oncoming person, while Asians will turn to the left. Further, people are frequently organized into sub-groups within crowds as they walk with their friends.

The Economist has an interesting article, The wisdom of crowds, that discusses recent studies of crowd behaviors. As the article explains:

If two opposing people guess each other’s intentions correctly, each moving to one side and allowing the other past, then they are likely to choose to move the same way the next time they need to avoid a collision. The probability of a successful manoeuvre increases as more and more people adopt a bias in one direction, until the tendency sticks. Whether it’s right or left does not matter; what does is that it is the unspoken will of the majority.

That is at odds with most people’s idea of being a pedestrian. More than any other way of getting around—such as being crushed into a train or stuck in a traffic jam—walking appears to offer freedom of choice. Reality is more complicated. Whether stepping aside to avoid a collision, following the person in front through a crowd or navigating busy streets, pedestrians are autonomous yet constrained by others. They are both highly mobile and very predictable. “These are particles with a will,” says Dirk Helbing of ETH Zurich, a technology-focused university.

As the article points out, the cognitive ability of people in crowds limits the accuracy of purely particle based (like the above video) simulations of crowd movement. Of course, this difference really comes into play when considering emergency evacuations:

Mr Moussaid’s solution to such complexity has been to build a model based less on the analogy between humans and particles and more on cognitive science. Agents in this new model are allowed to “see” what’s in front of them; they then try to carve a free path through the masses to get to their destination. This approach produces the same effects of lane-formation in crowds as the physics-based models, but with some added advantages.

In particular, boffins think it could help make emergency evacuations safer. Simulating evacuations is a big part of what pedestrian modellers do—the King’s Cross underground fire in London in 1987 gave the field one of its starting shoves. One big danger in an emergency is that people will follow the crowd and all herd towards a single exit. That in turn means that the crowd may jam as too many people try to force their way through a single doorway.

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.

The cognitive-science model offers a more palatable option, that of experimenting with the effects of changes in people’s visual fields. Mr Moussaid speculates that adaptable lighting systems, which use darkness to repel people and light to attract them, could be used to direct them in emergencies, for example. 

 Finally, what discussion of walking would be complete without an example of Japanese Precision Walking?