How do users interact with products in a store? What is the best disposition of artwork in a museum? How can airports prevent queue formation at security checks? Predicting crowd behavior through proximity sensors is not that far ahead in the future. By Claudio Martella
A few weeks ago, I was at the airport of Milan flying out to Amsterdam. Unfortunately, even though I was on time, I started worrying about missing my flight because the queue at the security check was so long.
As I was in line, I wondered why the managers of the airport did not send some personnel to help those of us who had an imminent flight or just open some more security check booths to speed up the process. I also wondered why they did not predict such bottleneck way in advance. After all, it should be enough to recognize that the rate at which people were arriving at the end of the queue was higher than the rate at which people were leaving the head of the queue. This can only mean a queue is forming and it is growing over time.
Many collective behaviors are characterized, among other things, by a strong spatiotemporal nature. The behavior of the individuals is influenced by the environment in which they are embedded, which includes other individuals. Typically, objects nearby tend to influence behavior more than objects far away. In the case of a queue, we tend to enter the queue behind the person who arrived before us, and our turn to leave the queue comes when we have nobody in front of us. In the case of pedestrian lanes on a sidewalk, we tend to stay close to the people close to us walking in our same direction, and we stay away from the people passing by walking in the other direction to avoid collisions.
Besides, it is not just about low-level “crowd dynamics” like queues and lanes, but this is also true for higher-level forms of social behavior: we also stay face-to-face with the people we mingle with and share offices with the people we work with. And it is true also for relationships between people and objects: we face the appliances we interact with and we keep close the objects we care about and need to grab often. In other words, it is important to measure the context in which a person is immersed to understand her behavior. To this end, we need to identify who and what is in somebody’s proximity, and how these change over time. But how?
© UA Magazine
EWIDS (Very large wireless sensor networks for well-being)
Ook dit is een COMMIT/ project