Unboxing the graph: Neural Relational Inference for Mobility Prediction. (arXiv:2201.10307v1 [cs.LG])

Web: http://arxiv.org/abs/2201.10307

Predicting the supply and demand of transport systems is vital for efficient
traffic management, control, optimization, and planning. For example,
predicting where from/to and when people intend to travel by taxi can support
fleet managers to distribute resources; better predicting traffic
speeds/congestion allows for pro-active control measures or for users to better
choose their paths. Making spatio-temporal predictions is known to be a hard
task, but recently Graph Neural Networks (GNNs) have been widely applied on
non-euclidean spatial data. However, …

arxiv graph neural prediction