LLM-enhanced spatio-temporal learning for grid-level docked bike sharing demand prediction
Short-term bike-sharing demand forecasting is complicated by spatial-temporal non-stationarity and the practical difficulty of incorporating unstructured external text into numerical pipelines. Conventional approaches rely on historical flow sequences and fixed graph structures, thereby constraining their accuracy when anomalous social events perturb normal travel patterns. We propose a forecasting framework in which a Large Language Model (LLM) drives a semantic shockwave mechanism that converts free-form urban text, such as municipal event schedules, local news, and transit bulletins, into quantified spatial-temporal perturbation fields. The LLM extracts three physically interpretable p...
| Authors | Xuxilu Zhang, Francesc Soriguera |
|---|---|
| Primary category | cs.LG |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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