Bike-Sharing Demand Forecasting Using Machine Learning - Angarita-Zapata, Juan S.; Torrent-Fontbona , Ferran; Machón , Beatriz; Benito Sanchez, Francisco (Street Art & Urban Creativity, 2025)
Bike-share systems work best when users can count on finding a bike and an open dock. That simple expectation is hard to meet during peak times and special events. We describe a practical, short-term demand-forecasting pipeline built using Machine Learning (ML) and deployed in Valladolid, Spain, as part of the EU-funded SPINE project. Using anonymized station-to-station records from the city’s bike-sharing system, we generate station-level forecasts over 60-minute horizons and feed those predictions to operations dashboards. Moreover, rebalancing strategies are evaluated in a simulation environment. We summarize the data flow and model architecture, show representative network-level results, and outline how the system can be used by the bike-sharing operator to support day-to-day decisions. We close with lessons learned and priorities for the next steps.