针对共享单车需求随时间变化的预测难题，提出鲁棒生成式域适应方法。
Robust OT-Guided Generative Residual Domain Adaptation for Bike-Sharing Demand Prediction under Temporal Domain Shift
- 基于最优传输引导残差迁移，聚焦目标域锚点与小样本标签数据。
- 在2025-2026年任务上实现最低MAE，优于多数同类方法。
- 对异常无标签数据更稳定，适合真实场景中噪声时间迁移。
基于历史站点-小时数据训练的共享单车模型在后期部署时因出行模式随时间演变而性能下降。本文将2021至2026年纽约市共享单车需求预测视为时间域适应问题，提出Gen-ROTDA——一种鲁棒的最优传输引导残差域适应框架。该方法利用少量有标签目标数据构建目标域站点-时间锚点，迁移残差而非原始需求，采用确定性保标签残差特征生成器，并在训练前剔除高成本传输匹配项。实验对比了锚点仅用、源端仅用、目标端仅用、微调、MMD适应、Sinkhorn OTDA、ROTDA和Gen-OTDA等方法。Gen-ROTDA在2025–2026主任务上达到最低平均绝对误差（MAE），在跨年任务中整体表现最优；尽管微调与MMD仍为强基线。当目标域无标签数据存在异常时，Gen-ROTDA显著优于非鲁棒的OT方法，表明鲁棒传输对噪声时间迁移具有重要意义。
原文摘要 · Abstract (English)
Bike-sharing models trained on historical station-hour data may degrade when deployed in later years because travel patterns change over time. This paper studies March Citi Bike demand prediction from 2021 to 2026 as a temporal domain adaptation problem and proposes Gen-ROTDA, a robust optimal transport-guided residual domain adaptation framework. The method fits a target-domain station-time anchor with a small labeled target subset, transfers residual rather than raw demand, applies a deterministic label-preserving residual feature generator, and trims high-cost transport matches before training the final residual predictor. Experiments compare Gen-ROTDA with anchor-only, source-only, target-only, fine-tuning, MMD adaptation, Sinkhorn OTDA, ROTDA, and Gen-OTDA. Gen-ROTDA achieves the lowest MAE on the main 2025 to 2026 task and is the best OT-family method on average across multi-year tasks, although fine-tuning and MMD adaptation remain strong overall baselines. Under abnormal target-unlabeled records, Gen-ROTDA is much more stable than non-robust OT variants, suggesting that robust transport is useful for noisy temporal transfer in bike-sharing demand prediction.
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