[Day 161] Learning about GANs' use in generating OD demand matrix
Hello :) Today is Day 161! A quick summary of today: explored how Generative Adversarial Networks can be used for OD demand matrix generation found that the original graph attention network has limitations and read the paper for GATv2 First paper - A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction [ ScienceDirect ] Introduction OD-based prediction focuses on estimating travel demand between specific origin and destination regions, which is useful for understanding inter-regional travel patterns. This approach requires consideration of complex spatio-temporal correlations, making it more challenging than region-level prediction. Although some progress has been made in OD-level prediction, several key issues remain: Practical OD Demand Forecasting: Previous studies often only consider relationships between OD pairs, rather than between OD regions. Accurate OD demand matrices and inter-regional relationships need t