<p>Bike-sharing systems have gained popularity as a solution for short-distance urban travel, highlighting the need for precise demand prediction. Most existing methods predict bike-sharing demand across all regions without adequately accounting for spatial heterogeneity, meaning they overlook that different regions may have distinct and skewed demand distributions. Moreover, these models frequently miss the temporal heterogeneity introduced by varying demand patterns, as they tend to model temporal correlations with a uniform parameter set across all time periods, failing to account for the dynamic fluctuations in demand over time. To address these shortcomings, we introduce a novel Robust Spatio-Temporal Demand Prediction (RST) model that enhances bike-sharing demand prediction. The proposed approach employs a convolutional spatio-temporal graph, which integrates the features from multiple sources, including Points of Interest (POIs), road networks, and weather. Upon that, it proposes a scheme to classify the stations based on their traffic irregularity. The proposed scheme captures the traffic dependencies between stations by generating an embedding sequence from the historical data and ignoring correlations to the low-relevant stations, ensuring accurate predictions. Furthermore, the model introduces a dynamic hierarchical structure, where an additional retraining layer is activated only for stations with underperforming predictions, leveraging data from similar stations to refine and improve accuracy. This two-layered hierarchical structure ensures robustness and adaptability in complex scenarios such as different weather conditions or events. Our methodology sets a new standard in demand prediction for bike-sharing, demonstrating significant improvements in prediction accuracy on real-world datasets from New York City’s Citi-Bike and Beijing’s BJ-Bike. The results validate our model’s superior performance, offering a scalable and effective strategy for enhancing urban mobility.</p>