Mobility Data

Mobility data, encompassing spatiotemporal records of human movement, is analyzed to understand travel patterns, predict future locations, and detect anomalies. Current research emphasizes developing robust and privacy-preserving methods using deep learning architectures like recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs), often incorporating large language models (LLMs) to enhance semantic understanding and prediction accuracy. This field is crucial for improving urban planning, transportation systems, public health initiatives, and personalized services, while simultaneously addressing significant privacy concerns associated with the sensitive nature of location data.