The researchers at National University of Singapore have employed deep learning technology to model and forecast human movement and then use that information to optimise evacuation, reduce accidents, and ease traffic congestion during emergency situations. The research team is particularly interested in modelling how people would run or flee in such circumstances. According to the lead head, Associate Professor Gary Tan, NUS Computing, when people are in a panic, they act extremely differently and try to anticipate what would happen when. The application’s data-driven approach makes it easier to build crowd management tactics that are more effective and delivers a more accurate prediction of human reactions in a crisis. The framework interprets the movement patterns of pedestrians in real-world video feeds and converts them into data that can be used in a virtual simulator. The technology uses deep learning techniques to identify objects in specific video frames and accurately track them across the video feed. They recreate settings and imitate actions that would be too expensive or risky to be carried out in real life. The methodology is distinctive because, in contrast to earlier pedestrian simulation methods, it takes a data-driven approach and aims to investigate human behaviour directly from real-life footage. Since they are adapted from real video, this raises the level of realism.