Enhancing Demand Prediction in Open Systems by Cartogram-aided Deep
  Learning
Sangjoon Park Yongsung Kwon Hyungjoon Soh Mi Jin Lee Seung-Woo Son
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.