demand-prediction

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.

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.