# Spatio-Temporal Graph Convolutional Network Combined Large Language Model: A Deep Learning Framework for Bike Demand Forecasting

arXiv: https://arxiv.org/abs/2403.15733 · HTML: https://mlsanity.com/paper/2403.15733/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [LLM-enhanced spatio-temporal learning for grid-level docked bike sharing demand prediction](https://mlsanity.com/paper/2610.03834/) (2026)

## Abstract (arXiv)

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.