Stochastic one-way carsharing systems with dynamic relocation incentives through preference learning.

The development of carsharing services is expected to achieve greater resource efficiency and provide a sustainable solution for future mobility systems. However, operators inevitably face the imbalance between demand and supply in one-way carsharing systems (CSSs). Also, it is challenging for them to make quick and efficient operational decisions when both travel time and trip requests are uncertain. This study leverages historical and online data and proposes a learning-based methodology to quickly make real-time decisions for CSSs, including vehicle assignment, relocation, and user incentive decisions. Compared to the literature in approximate dynamic programming (ADP), which mostly focuses on uncertainty in trip requests, we propose an offline–online ADP approach to consider the temporal and spatial uncertainty in both trip requests and travel time. To tackle our high-dimensional problem, we integrate an online look-ahead policy into the offline value function approximation (VFA) policy to produce a computational tractable high-quality dynamic fleet management policy. Furthermore, a user-based relocation strategy is investigated to rebalance the fleet distribution to meet the demand better. Specifically, we aim to solve the optimal incentives the operator could offer to users to relocate cars, while user preferences towards relocation incentives are generally unknown in practice. We further enhance our anticipatory policy by developing an online module via Bayesian learning that learns the preference model on the fly using users' revealed preference data collected online. The numerical experiments in Singapore demonstrate our offline–online ADP approach improves the solution quality and significantly compared to offline VFA policy. The results also confirm the importance of incorporating uncertainty in travel time. The benefits of the user-based relocation scheme using the Bayesian learning method using online data to enhance anticipatory decisions and learn unknown user preferences are also illustrated. It quickly and efficiently learns the user preferences, which further reduces the relocation cost and increases the profit. • Consider both demand uncertainty and travel time uncertainty in one-way carsharing systems. • Investigate a user-based relocation strategy combined with staff-based relocation. • Propose an offline–online ADP solution framework to make real-time operational decisions quickly. • Learn users' willingness to relocate through Bayesian learning. • Demonstrate the performance of our solution framework under various scenarios. [ABSTRACT FROM AUTHOR]