Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy

Bike-sharing systems (BSS) are a key component of sustainable urban mobility, but their effectiveness is often hindered by the operational challenge of fleet rebalancing, which leads to service failures and high costs. This study proposes and evaluates a dynamic, rule-based user-incentive policy designed to mitigate system imbalances. We developed a data-driven discrete-event simulation model, calibrated with over one million real-world trips from the Oslo city bike system, to serve as a virtual laboratory for testing the policy's performance. The simulation results, averaged over multiple replications, demonstrate the policy's significant effectiveness. Compared to a baseline scenario with a dock unavailability rate of 6.27%, the incentive policy with a 75% user acceptance rate reduced this failure rate to a negligible 0.25%, representing a 96% improvement in service quality for returning users. This work validates that crowdsourcing rebalancing efforts through user incentives, evaluated via high-fidelity simulation, is a powerful and practical approach for enhancing the operational efficiency and reliability of station-based BSS.