  This study presents a new deep learning framework, combining Spatio-Temporal
Graph Convolutional Network (STGCN) with a Large Language Model (LLM), for bike
demand forecasting. Addressing challenges in transforming discrete datasets and
integrating unstructured language data, the framework leverages LLMs to extract
insights from Points of Interest (POI) text data. The proposed STGCN-L model
demonstrates competitive performance compared to existing models, showcasing
its potential in predicting bike demand. Experiments using Philadelphia
datasets highlight the effectiveness of the hybrid model, emphasizing the need
for further exploration and enhancements, such as incorporating additional
features like weather data for improved accuracy.
