GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap. Standard models rely on transductive feature initializations that tie travel demand to fixed network topologies, preventing seamless transfer to new urban environments. To overcome this structural limitation, this research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand E
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- LinkedLinked via arxiv author · 85%Alessandro Scalese →
“GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models”
- LinkedLinked via arxiv author · 85%Santhanakrishnan Narayanan →
“GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models”
- LinkedLinked via arxiv author · 85%Constantinos Antoniou →
“GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models”