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Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems

arXiv机器学习 2026-08-28 12:00 1 阅读 查看原文

Computer Science > Machine Learning

arXiv:2608.26451 (cs)

Title:Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems

Authors:Man Luo, Yixuan Zhao
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Abstract: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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2608.26451 [cs.LG]
  (or arXiv:2608.26451v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.26451

Submission history

From: Yixuan Zhao [view email]
[v1] Wed, 26 Aug 2026 22:57:40 UTC (7,136 KB)
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