{"slug": "toward-equitable-low-carbon-mobility-fairness-aware-demand-prediction-for-bike", "title": "Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems", "summary": "Researchers propose FairGIN, a fairness-aware graph neural network for demand prediction in expanding bike-sharing systems, achieving state-of-the-art accuracy on NYC and Seattle data while reducing income-based disparities. The model integrates expansion-simulated training, attention-based knowledge transfer, and fairness-aware optimization to address cold-start prediction and equitable resource allocation.", "body_md": "arXiv:2608.26451v1 Announce Type: new\nAbstract: 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, a fairness-aware graph neural network for demand prediction in expanding bike-sharing systems. 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 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.", "url": "https://wpnews.pro/news/toward-equitable-low-carbon-mobility-fairness-aware-demand-prediction-for-bike", "canonical_source": "https://arxiv.org/abs/2608.26451", "published_at": "2026-08-28 04:00:00+00:00", "updated_at": "2026-08-28 04:21:15.065931+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["FairGIN", "NYC", "Seattle"], "alternates": {"html": "https://wpnews.pro/news/toward-equitable-low-carbon-mobility-fairness-aware-demand-prediction-for-bike", "markdown": "https://wpnews.pro/news/toward-equitable-low-carbon-mobility-fairness-aware-demand-prediction-for-bike.md", "text": "https://wpnews.pro/news/toward-equitable-low-carbon-mobility-fairness-aware-demand-prediction-for-bike.txt", "jsonld": "https://wpnews.pro/news/toward-equitable-low-carbon-mobility-fairness-aware-demand-prediction-for-bike.jsonld"}}