Improving demand forecasting in open systems with cartogram-enhanced deep learning
- Title
- Improving demand forecasting in open systems with cartogram-enhanced deep learning
- Author
- 이미진
- Keywords
- Cartogram-enhanced deep learning; Forecasting open systems; Social dynamics
- Issue Date
- 2024-05-29
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Citation
- CHAOS SOLITONS & FRACTALS, v. 184, article no. 115032, page. 1-8
- Abstract
- Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data -driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long -period prediction, which has not been achieved before. We apply this method to public bicycle rental -and -return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.
- URI
- https://www.sciencedirect.com/science/article/pii/S0960077924005848https://repository.hanyang.ac.kr/handle/20.500.11754/191747
- ISSN
- 0960-0779
- DOI
- 10.1016/j.chaos.2024.115032
- Appears in Collections:
- COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E](과학기술융합대학) > APPLIED PHYSICS(응용물리학과) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML