Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning
This work addresses demand forecasting for shared transport systems like public bicycles, offering incremental improvements in prediction capabilities for urban planning and management.
The study tackled the problem of predicting rental and return patterns in public bicycle systems, which is challenging due to system openness and imbalanced usage, by proposing a deep learning framework that leverages cartogram approaches to enable predictions for new stations and long periods, achieving improved accuracy across different time scales.
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.