Reconstructing hourly traffic on every street of a capital from a sparse counting network: a graph neural network applied to Paris
Micro-scale air quality models require hourly traffic data on every road segment, whereas permanent counters cover only a small part of the network. A graph neural network (GNN) was developed to reconstruct hourly traffic flows on the 32,163 road segments of Paris intra-muros from the 1,573 segments equipped with a counter (4.9%). The model adapts the Graph Neural Network for Urban Interpolation…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
