Summary of Improving Demand Forecasting in Open Systems with Cartogram-enhanced Deep Learning, by Sangjoon Park et al.
Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning
by Sangjoon Park, Yongsung Kwon, Hyungjoon Soh, Mi Jin Lee, Seung-Woo Son
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Physics and Society (physics.soc-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep learning framework leverages cartogram approaches to predict rental and return patterns in public bicycle systems, addressing the challenges of openness and imbalanced usage patterns. The framework incorporates batch attention and modified node feature updates in a spatial-temporal convolutional graph attention network, allowing for improved prediction accuracy across different time scales. This approach enables long-period prediction, which has not been achieved before, and demonstrates potential applications in predicting temporal patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict when people will rent or return bicycles at public stations is developed. This helps cities manage their bike-sharing systems better. The method uses special maps that show how different parts of the city are connected. It can even predict what will happen at new bike station locations before any data is collected. The approach was tested in Seoul, South Korea, and showed it could accurately predict when people would rent or return bicycles. |
Keywords
* Artificial intelligence * Attention * Deep learning * Graph attention network
