公平な低炭素モビリティに向けて：拡張するバイクシェアシステムの公平性を考慮した需要予測
Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems (原題)
Man Luo, Yixuan Zhao
🤖 gxceed AI 要約
日本語
バイクシェアリングは低炭素都市モビリティの重要要素だが、新規ステーションの需要予測と公平な資源配分に課題がある。提案手法FairGINは、拡張シミュレーション学習、注意ベース知識転移、公平性最適化を統合したグラフニューラルネットワークで、冷開始問題と所得格差を同時に解決する。NYCとシアトルの実験で、予測精度を維持しつつ所得ベースの不公平を大幅に削減することを実証した。
English
Bike-sharing is key for low-carbon urban mobility, but expansion faces cold-start prediction and equitable allocation challenges. FairGIN, a fairness-aware GNN, integrates expansion-simulated training, attention-based knowledge transfer, and fairness-aware optimization to address both. Experiments on NYC and Seattle show state-of-the-art accuracy while substantially reducing income-based disparities.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、都市部でのシェアサイクル導入が進むが、地域間格差やインフラ整備の公平性が課題。本手法は、SSBJやESG情報開示における社会的側面（S）の評価にも応用可能で、自治体や事業者の設備投資判断に示唆を与える。
In the global GX context
Globally, this work contributes to equitable low-carbon mobility, aligning with TCFD/ISSB's social aspects and sustainable urban development goals. It offers a data-driven approach to ensure that climate mitigation efforts do not exacerbate existing inequalities, relevant for cities worldwide.
👥 読者別の含意
🔬研究者:Provides a novel fairness-aware GNN framework for demand prediction in expanding networks, with implications for equitable climate action.
🏢実務担当者:Offers a tool for bike-sharing operators and urban planners to optimize station placement while addressing equity concerns.
🏛政策担当者:Highlights how AI can support equitable low-carbon mobility policies, informing urban infrastructure investment decisions.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48550/arxiv.2608.26451first seen 2026-08-30 04:41:04
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