A GatedGCN metamodel approximates Stochastic User Equilibrium traffic flows on the Sioux Falls network and generalizes to out-of-distribution capacity and speed changes, but not to demand changes.
Large- scale multimodal transportation network models and algorithms-part i: The combined mode split and traf- fic assignment problem,
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Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment
A GatedGCN metamodel approximates Stochastic User Equilibrium traffic flows on the Sioux Falls network and generalizes to out-of-distribution capacity and speed changes, but not to demand changes.