Crowd Simulation

Crowd simulation aims to realistically model the collective behavior of large groups of individuals, primarily to understand and predict their movements in various scenarios. Current research emphasizes improving the behavioral diversity and realism of simulations, focusing on enhanced models like the Social Force Model and its variants, incorporating visual information via deep learning (e.g., Temporal Convolutional Networks), and leveraging graph-based approaches for efficient navigation in constrained environments. These advancements have significant implications for urban planning, security applications (e.g., crowd monitoring and event detection), robotics (e.g., robot-human interaction), and the development of more realistic virtual environments.