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The paper proposes FairGIN, a fairness-aware graph neural network for demand prediction in expanding bike-sharing systems, addressing cold-start expansion and spatial inequities to promote equitable low-carbon mobility.
STAGformer introduces a spatio-temporal agent graph transformer with linear complexity for bike-sharing demand forecasting, outperforming baselines on NYC and Chicago datasets.
This paper proposes Gen-ROTDA, a robust optimal transport-guided residual domain adaptation framework for predicting bike-sharing demand under temporal domain shift, achieving improved stability and accuracy compared to baselines, especially with noisy target data.