Reliable short-term demand forecasting is essential for managing shared micro-mobility services and ensuring responsive, user-centered operations. This study introduces T-STAR (Two-stage Spatial and Temporal Adaptive contextual Representation), a novel transformer-based probabilistic framework designed to forecast station-level bike-sharing demand at a 15-minute resolution. T-STAR addresses key challenges in high-resolution forecasting by disentangling consistent demand patterns from short-term fluctuations through a hierarchical two-stage structure. The first stage captures coarse-grained hourly demand patterns, while the second stage improves prediction accuracy by incorporating high-frequency, localized inputs, including recent fluctuations and real-time demand variations in connected metro services, to account for temporal shifts in short-term demand. Time series transformer models are employed in both stages to generate probabilistic predictions. Extensive experiments using Washington D.C.'s Capital Bikeshare data demonstrate that T-STAR outperforms existing methods in both deterministic and probabilistic accuracy. The model exhibits strong spatial and temporal robustness across stations and time periods. A zero-shot forecasting experiment further highlights T-STAR's ability to transfer to previously unseen service areas without retraining. These results underscore the framework's potential to deliver granular, reliable, and uncertainty-aware short-term demand forecasts, which enable seamless integration to support multimodal trip planning for travelers and enhance real-time operations in shared micro-mobility services.

翻译：可靠的短期需求预测对于管理共享微出行服务、确保响应迅速且以用户为中心的运营至关重要。本研究提出了T-STAR（两阶段时空自适应上下文表示），这是一种新颖的基于Transformer的概率框架，旨在以15分钟分辨率预测站点级共享单车需求。T-STAR通过分层两阶段结构，将一致的需求模式与短期波动分离，从而解决了高分辨率预测中的关键挑战。第一阶段捕捉粗粒度的每小时需求模式，而第二阶段通过纳入高频局部输入（包括近期波动和关联地铁服务的实时需求变化）来提高预测精度，以考虑短期需求的时间偏移。两个阶段均使用时序Transformer模型生成概率预测。利用华盛顿特区Capital Bikeshare数据进行的广泛实验表明，T-STAR在确定性和概率准确性方面均优于现有方法。该模型在不同站点和时段表现出强大的时空鲁棒性。一项零样本预测实验进一步凸显了T-STAR能够迁移到先前未见过的服务区域而无需重新训练。这些结果强调了该框架在提供细粒度、可靠且具有不确定性感知的短期需求预测方面的潜力，从而能够无缝集成以支持旅客的多模式出行规划，并增强共享微出行服务的实时运营能力。