SEDAN fuses graph-based urban semantics and spatial structure inside a conditional diffusion model to generate behaviorally plausible and geographically coherent OD matrices, reporting a 7.38% RMSE gain over the WEDAN baseline on U.S. city data.
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Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
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Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation
SEDAN fuses graph-based urban semantics and spatial structure inside a conditional diffusion model to generate behaviorally plausible and geographically coherent OD matrices, reporting a 7.38% RMSE gain over the WEDAN baseline on U.S. city data.
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Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.