发表机构
University of Exeter（埃克塞特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对扩展共享单车系统的冷启动预测与公平资源分配挑战，提出公平感知图神经网络FairGIN，通过三类组件实现高精度需求预测并降低收入差距。
AI 中文摘要
共享单车系统是低碳城市出行的重要组成部分，但其持续扩展带来了冷启动预测和公平资源分配两方面的挑战。新部署的站点缺乏历史骑行量记录，导致基于图的模型在不断演化的网络上训练与推理不匹配；历史需求还可能编码结构性不平等，低收入社区骑行量较低可能反映基础设施接入有限而非潜在需求不足，直接基于此类数据训练的模型可能加剧现有的出行差距。我们提出FairGIN，一种面向扩展共享单车系统需求预测的公平感知图神经网络，它集成了三个组件：扩展模拟增量训练在训练过程中随机模拟网络扩展，以缩小冷启动分布差距；注意力机制知识迁移结合站点自适应温度缩放与正交嵌入对齐，将数据丰富的现有站点的表示迁移到数据稀疏的新站点；公平感知优化引入收入分层正则化和公平校准部署分数，以支持更具包容性的站点布局。在纽约和西雅图的实验表明，FairGIN在各类扩展场景下实现了最先进的预测精度，同时大幅降低了基于收入的差距，且未损害整体系统效率。
英文摘要
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.
