Improving demand forecasting in open systems with cartogram-enhanced deep learning

• Published in: Chaos, solitons and fractals Vol. 184; p. 115032
• Main Authors: Park, Sangjoon, Kwon, Yongsung, Soh, Hyungjoon, Lee, Mi Jin, Son, Seung-Woo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024
• Subjects: Cartogram-enhanced deep learning; Forecasting open systems; Social dynamics; Cartogram-enhanced deep learning; Social dynamics; Forecasting open systems
• ISSN: 0960-0779
• DOI: 10.1016/j.chaos.2024.115032

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. •Predicting temporal patterns of demands in public bicycles as a representative of open systems.•Proposing deep learning framework to improve prediction accuracy.•Leveraging cartogram approaches to predict future demand of untrained points and long-period patterns for all points.