Spatiotemporal Demand Forecasting and Proactive Rebalancing Optimization for Urban Bike-Sharing: An Integrated Machine Learning Framework

This study presents an integrated, weather-aware framework for urban bike-sharing systems that combines Random Forest-based station-level demand forecasting with zone-level linear programming rebalancing optimization. Using 2024 Jersey City Citi Bike data, the model achieves robust predictive performance and a 13.4% improvement in service fulfillment rate alongside an 18.2% reduction in daily rebalancing vehicle trips compared to a reactive baseline, offering a practical, deployable solution for proactive fleet management.