September 22, 2025 by The Korea Advanced Institute of Science and Technology (KAIST)
Collected at: https://techxplore.com/news/2025-09-ai-crowd-disasters.html
To prevent crowd crush incidents like the Itaewon tragedy, it’s crucial to go beyond simply counting people and to instead have a technology that can detect the real-inflow and movement patterns of crowds. 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.
The research was presented at the Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, and a research team led by Professor Jae-Gil Lee from the School of Computing developed the AI technology that can more accurately predict crowd density.
The dynamics of crowd gathering cannot be explained by a simple increase or decrease in the number of people. Even with the same number of people, the level of risk changes depending on where they are coming from and which direction they are heading.
Professor Lee’s team expressed this movement using the concept of a time-varying graph. This means that accurate prediction is only possible by simultaneously analyzing two types of information: node information (how many people are in a specific area) and edge information (the flow of people between areas).
In contrast, most previous studies focused on only one of these factors, either concentrating on how many people are gathered right now or which paths are people moving along. 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.
To achieve this, the team developed a bi-modal learning method. This technology simultaneously considers population counts (node information) and population flow (edge information), while also learning spatial relationships (which areas are connected) and temporal changes (when and how movement occurs).
Specifically, the team introduced a 3D contrastive learning technique. This allows the AI to learn not only 2D spatial (geographical) information but also temporal information, creating a 3D relationship.
As a result, the AI can understand not just whether the population is large or small right now, but what pattern the crowd is developing into over time. This allows for a much more accurate prediction of the time and place where congestion will occur than previous methods.
The research team built and publicly released six real-world datasets for their study, which were compiled from sources such as Seoul, Busan, and Daegu subway data, New York City transit data, and COVID-19 confirmed case data from South Korea and New York.
The proposed technology achieved up to a 76.1% improvement in prediction accuracy over recent state-of-the-art methods, demonstrating strong performance.
Professor Jae-Gil Lee stated, “It is important to develop technologies that can have a significant social impact. I hope this technology will greatly contribute to protecting public safety in daily life, such as in crowd management for large events, easing urban traffic congestion, and curbing the spread of infectious diseases.”
More information: Youngeun Nam et al, Bi-Modal Learning for Networked Time Series, Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (2025). DOI: 10.1145/3711896.3736856
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