Bike-sharing systems are an important component of low-carbon urban mobility, but continued expansion creates challenges in both cold-start prediction and equitable resource allocation.
Newly deployed stations lack historical ridership records, causing a mismatch between training and inference for graph-based models on evolving networks.
Historical demand may also encode structural inequalities, as lower ridership in low-income neighborhoods can reflect limited infrastructure access rather than weak latent demand.
Models trained directly on such data may therefore reinforce existing mobility disparities.
We propose FairGIN
FairGIN integrates three components.
- Expansion-Simulated Increment Training stochastically simulates network expansion during training to reduce the cold-start distribution gap.
- Attention-Based Knowledge Transfer combines station-adaptive temperature scaling with orthogonal embedding alignment to transfer representations from data-rich existing stations to data-sparse new stations.
- Fairness-Aware Optimization introduces income-stratified regularization and an equity-calibrated deployment score to support more inclusive station placement.
Experiments
Experiments on NYC and Seattle demonstrate that FairGIN achieves state-of-the-art predictive accuracy across diverse expansion scenarios while substantially reducing income-based disparities without compromising overall system efficiency.