Abstract

Shared micromobility systems use lightweight vehicles (e.g., bikes/scooters) to offer last-mile transportation. Due to flexible pick-up/drop-off customer behavior, such demand uncertainty often causes imbalances betweenvehicle supply and demand. To prevent such case, the operator can leverage vehicle repositioning policy in addition to pricing control policy (incentives) to relocate vehicles. This research proposes a data-driven RS model to address a multi-period, joint vehicle pricing and repositioning problem for shared micromobility. Specifically, historical data and side information are leveraged to predict uncertain demand with a linear prediction model. Based on the estimated distribution, a residual-based RS model addresses distributional ambiguity of random residuals, and an estimation-fortified RS model is developed to control prediction error at the expense of target loss. Tractable model reformulation and recourse adaption (affine/biaffine) are going to be developed.

Key words

Shared Micromobility，Vehicle Repositioning, Robust Satisficing, Data-driven Decision-making