Abstract
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 data-driven 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 calibrates 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 computational efficiency significantly compared to offline VFA policy. The results also confirm the importance of incorporating uncertainty in travel time. The benefits of using online data to enhance anticipatory decisions and learn unknown user preferences are also illustrated. After a few online iterations, the preference parameters converge to the true values, which further reduces the relocation cost and increases the profit.
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