Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks

arXiv:2303.04040v2 Announce Type: replace
Abstract: Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably exists in travel demand prediction. To fill this gap, this study proposes a framework of probabilistic graph neural networks (Prob-GNN) to quantify the spatiotemporal uncertainty of travel demand. This Prob-GNN framework is substantiated by deterministic and probabilistic assumptions, and empirically applied to the task of predicting the transit and ridesharing demand in …

abstract accuracy arxiv cs.lg demand framework gap gnn graph graph neural networks networks neural networks prediction quantification stat.ap stat.ml studies study travel type uncertainty