A two-stage incentive mechanism for rebalancing free-floating bike sharing systems: Considering user preference.

• Propose three incentive mechanisms for rebalancing free-floating bike sharing systems. • Model users' behavioral responds to incentives. • Conduct a survey study to test uses' attitudes to different incentive mechanisms. • Pick-up incentives perform best, so it is unnecessary to incentivize a user twice. • User preference narrows the advantage of pick-up incentives. • The incentive mechanism is robust to prediction error. Free-floating bike sharing (FFBS) provides citizens with a flexible way for traveling. The operational performance of such systems is impacted by the imbalance problem of demand and supply. This paper proposes the first two-stage incentive mechanism to improve the service quality of FFBS. While inspired by the user-based rebalancing approach in station-based bike sharing (SBBS), our work is different from existing studies because location optimization is a new problem in FFBS and user preference is considered for the first time. We propose three mechanisms that are pick-up incentives, drop-off incentives, and two-stage incentives. When modeling users' behavioral responses to incentive mechanisms, we think users prefer drop-off incentives to pick-up incentives, which is supported by a survey study. Each incentive offer is composed of a location and a price. To decide the suggested origin or destination, we first figure out the supply and demand of all valid locations based on Radiant Service Theory (RST) and then search the most problematic location comparing current inventory and future demand. The pricing scheme is modified Budgeted Procurement using Upper Confidence Bounds (BP-UCB). Simulation experiments validate the effectiveness of incentive mechanisms based on historical data from Capital BikeShare program. Results show that the service level of bike sharing systems can be significantly improved by three incentive mechanisms. The two-stage incentive mechanism unexpectedly shows no advantage over pick-up incentives. Sensitivity analysis further indicates that user preference will influence the ranking of two single-stage incentive mechanisms. [ABSTRACT FROM AUTHOR]