Enhancing Demand Prediction in Open Systems by Cartogram-aided Deep Learning

arXiv:2403.16049v1 Announce Type: new
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 …

abstract arxiv challenge challenges cs.lg data data-driven deep learning demand domains frameworks machine machine learning patterns physics.soc-ph prediction statistical systems temporal transport type