Human Mobility Forecasting

Human mobility forecasting aims to predict people's movement patterns, crucial for optimizing transportation systems and urban planning. Recent research heavily utilizes deep learning models, particularly recurrent neural networks like LSTMs and GRUs, often enhanced by incorporating natural language processing techniques. This involves translating numerical mobility data into textual descriptions, leveraging the power of large language models to identify complex patterns and improve prediction accuracy. The resulting advancements offer improved efficiency in areas like traffic management and resource allocation, while also addressing privacy concerns through differentially private training methods.