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.

翻译：本研究提出了一种结合时空图卷积网络（STGCN）与大型语言模型（LLM）的新型深度学习框架，用于自行车需求预测。针对离散数据集转换与非结构化语言数据整合的挑战，该框架利用大型语言模型从兴趣点（POI）文本数据中提取关键信息。所提出的STGCN-L模型在与现有模型的对比中展现出具有竞争力的性能，凸显了其在自行车需求预测方面的潜力。基于费城数据集的实验验证了该混合模型的有效性，并强调了进一步探索与优化的必要性——例如引入天气数据等额外特征以提升预测精度。