User-Behaviour-Based Dynamic Clustering Optimisation Algorithm for True Demand Prediction of Shared Bikes

Abstract

Dockless bike-sharing systems are increasingly important for sustainable urban mobility, yet they frequently suffer from spatial misallocation between supply and demand. Conventional demand forecasting relies primarily on historical trip records and therefore systematically underestimates true demand when users abandon bike-finding attempts. To address this limitation, we propose a user-behaviour based dynamic clustering optimisation algorithm that integrates observed riding behaviour with latent unmet demand through the concept of Golden Distance—a behaviourally derived, district-adaptive service-radius threshold representing the distance users are willing to walk to access a shared bike. Building on a prior AIoT-enabled demand-prediction framework, the method first applies HDBSCAN density-based clustering to discover intrinsic demand topology, then selectively refines only those clusters that violate Golden Distance coverage constraints via an elongation-aware adaptive k-medoids formulation. District-level true demand is predicted using an XGBoost regression model and subsequently downscaled to cluster level based on historical activity shares. Experiments on one full year of operational data from three Hong Kong districts (Tseung Kwan O, Sha Tin, and Tuen Mun) demonstrate substantial predictive improvements: RMSE reductions of 37.92%, 40.85%, and 49.49%, respectively, compared with the DBSCAN baseline, with an average RMSE reduction of 42.75% across districts. These results confirm that behaviour-aware, dynamically adaptive spatial clustering significantly improves true-demand estimation accuracy and provides more operationally meaningful service zones for dockless bike-sharing management.