A KAIST research team has successfully developed new AI crowd prediction technology that can be used not only for managing large-scale events and mitigating urban traffic congestion, but also for responding to infectious disease outbreaks. However, the research team emphasized that combining both is necessary to truly capture a dangerous situation. For example, a sudden increase in density in a specific alleyway, such as Area A, is difficult to predict with just current population data. But by also considering the flow of people continuously moving from a nearby area, Area B, towards Area A (edge information), its possible to pre-emptively identify the signal that Area A will soon become dangerous. This allows the AI to learn not only 2D spatial (geographical) information but also temporal information, creating a 3D relationship.