Crowd Flow
Crowd flow research focuses on understanding and predicting human movement patterns in dense environments, aiming to improve crowd management, urban planning, and safety. Current research employs diverse approaches, including convolutional neural networks (CNNs) for analyzing video data and identifying individual behaviors like pushing, and recurrent neural networks with attention mechanisms to model spatio-temporal dependencies in crowd flows for prediction. These models are often enhanced by incorporating elements like periodicity and laminar flow characteristics to improve accuracy and robustness, with recent work demonstrating that ensembles of large language models can achieve prediction accuracy comparable to human crowds. The insights gained are crucial for optimizing resource allocation, mitigating risks in crowded spaces, and enhancing overall urban functionality.