LLM-enhanced spatio-temporal learning for grid-level docked bike sharing demand prediction

A Large Language Model drives a semantic shockwave mechanism that converts free-form urban text into quantified spatial-temporal perturbation fields, which are injected into a Zero-Inflated Adaptive Spatio-Temporal Graph Convolutional Network for grid-level docked bike sharing demand prediction.