Mobility Prediction

Mobility prediction aims to forecast human movement patterns, crucial for optimizing urban planning, transportation management, and public health responses. Current research heavily utilizes deep learning, particularly large language models (LLMs) and graph neural networks (GNNs), to capture complex spatiotemporal dependencies and incorporate contextual information like user intentions and urban structures. These advancements improve prediction accuracy and offer insights into human behavior, but challenges remain in handling the inherent variability of human movement and ensuring model robustness against adversarial attacks. The field's impact extends to various applications, including resource allocation, personalized recommendations, and epidemic modeling.