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

TL;DR

This paper introduces a hybrid deep learning framework combining Spatio-Temporal Graph Convolutional Networks with Large Language Models to improve bike demand forecasting by integrating structured and unstructured data.

Contribution

It proposes a novel framework that effectively combines STGCN and LLMs, enabling better utilization of POI text data for demand prediction.

Findings

The hybrid model achieves competitive forecasting accuracy.

Incorporating POI text data enhances demand prediction.

The framework demonstrates effectiveness on Philadelphia bike demand data.

Abstract

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.