Spatio-Temporal Graph Convolutional Network Combined Large Language Model: A Deep Learning Framework for Bike Demand Forecasting
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910379562500096 |
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| author | Li, Peisen Pang, Yizhe Ren, Junyu |
| author_facet | Li, Peisen Pang, Yizhe Ren, Junyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15733 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatio-Temporal Graph Convolutional Network Combined Large Language Model: A Deep Learning Framework for Bike Demand Forecasting Li, Peisen Pang, Yizhe Ren, Junyu Social and Information Networks Computers and Society 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. |
| title | Spatio-Temporal Graph Convolutional Network Combined Large Language Model: A Deep Learning Framework for Bike Demand Forecasting |
| topic | Social and Information Networks Computers and Society |
| url | https://arxiv.org/abs/2403.15733 |