A Novel Demand Completion of Free-Float Bike-Sharing Based on Feature-Weighted K-Means Clustering

Free-floating bike-sharing, crucial in urban transport, often suffers from bike shortages. This paper’s demand model, employing feature-weighted K-means clustering, refines station boundaries. Utilizing matrix factorization theory and spatiotemporal data, it boosts demand accuracy, as seen in the empirical analysis of Beijing’s Jinyijiayuan residential area data set. This research offers a robust data basis for bike-sharing scheduling, advancing the system.