Flow Prediction

Flow prediction, encompassing the forecasting of various dynamic movements like traffic, crowds, and even fluid dynamics, aims to accurately model and predict these flows across space and time. Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs), often incorporating techniques like attention mechanisms and physics-guided learning to improve accuracy and interpretability. These advancements are crucial for optimizing urban planning, transportation management, autonomous driving systems, and various scientific simulations, offering more efficient and informed decision-making across diverse fields. The development of explainable models and the handling of incomplete or noisy data remain active areas of investigation.