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. ... Read more

Micro-delivery services offer promising solutions for on-demand city logistics, but their success relies on efficient real-time delivery operations and fleet management. On-demand meal delivery platforms seek to optimize real-time operations based on anticipatory insights into city-wide demand distributions. To address these needs, this study proposes a short-term predict-then-cluster framework for on-demand meal delivery services. In the forecasting stage, point and distributional predictions are generated using multivariate features, including temporal, contextual, and lagged-dependent features to capture complex demand dynamics. In the clustering stage, we propose two methods: Constrained K-Means Clustering (CKMC) and Contiguity Constrained Hierarchical Clustering with Iterative Constraint Enforcement (CCHC-ICE). These approaches form dynamic, geographically coherent clusters based on predicted demand, while accommodating user-defined operational constraints. Case studies on European and Taiwanese datasets demonstrate that lagged-dependent ensemble learning models perform robustly under sparse, zero-inflated demand conditions, whereas deep learning models such as LSTM excel in denser data regimes. Furthermore, results from the European case study highlight that incorporating distributional forecasts effectively captures demand uncertainty, thereby enhancing the quality of clustering outcomes and operational decision-making. By integrating demand uncertainty and operational constraints, the proposed framework delivers forward-looking, actionable insights for optimizing real-time meal delivery operations. The approach is adaptable to other on-demand platform-based city logistics and passenger mobility services, contributing to more sustainable and efficient urban operations. ... Read more