Researchers have unveiled groundbreaking insights into the dynamics of crowd movements, which could significantly enhance safety measures at mass gatherings. François Gu, a physicist from the École Normale Supérieure in Lyon, France, led a team that approached the study of crowds in a novel way. Instead of tracking each individual, Gu's team analyzed the crowd as a single free-flowing unit. This innovative approach likens crowds to a gel, allowing researchers to examine their behavior in unprecedented ways.
The team discovered that every 18 seconds, sections of the crowd, consisting of approximately 500 individuals, unintentionally moved in the same direction, forming a circular pattern. This phenomenon often occurs when people shift sideways to avoid being pushed by others around them. Such oscillations are more pronounced in dense, confined crowds, and their duration correlates with the length of time individuals remain confined.
The research team sought to determine if these patterns persisted in more critical situations by analyzing video footage from the 2010 Love Parade in Duisburg, Germany. Tragically, this event resulted in nearly two dozen fatalities when a stampede ensued. This study underscores the potential dangers of crowd crushes, as highlighted by other incidents in recent years. For instance, hundreds perished outside Mecca during the annual hajj pilgrimage in 2015, and more than 150 people lost their lives during a Halloween crowd surge in Seoul's bustling nightlife district in 2022.
The researchers also examined crowd movements at the San Fermín festival in Pamplona, Spain, which attracts over 5,000 participants. Beginning in 2019, they installed cameras to monitor the crowd's behavior. Their findings suggest it is possible to detect an increase in crowd activity up to 20 minutes before it occurs.
"So at some point, the whole crowd is gonna turn into on the right, on the left, and this creates this kind of oscillations," – Gu
This discovery indicates that crowds can exhibit predictable patterns even when densely packed.
"So our work can act as a detector of catastrophes," – Gu
The implications of this research are profound. By understanding how crowds move as a collective entity, event organizers can potentially identify warning signs before disasters occur.
"A crowd can be dense, but can be also safe," – Gu
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