EXPRESS: Hybrid Fleet Management and Vehicle Repositioning in Mobility-as-a-service Operations

While Mobility-as-a-Service (MaaS) platforms have revolutionized urban transportation and fundamentally transformed travelers’ experience and engagement, they encounter a significant challenge in maintaining a temporal and spatial balance between supply and demand, particularly with the inclusion of crowd-sourced freelance drivers. In this study, we propose a hybrid supply-side management problem that accounts for heterogeneous revenue dependent on surge pricing, where surge pricing not only determines per-trip earnings but also influences demand elasticity and overall revenue generation. This problem combines the physical repositioning of in-house vehicles with an incentivedriven approach (surge pricing in crowd-sourcing) for freelance vehicles. A multinomial logit (MNL) model is employed to model the repositioning behavior of crowd-sourced drivers, and reformulated as a low-level equilibrium problem embedded in a bi-level program. To characterize the surge price-dependent uncertain demand, a tailored residual-based Wasserstein ambiguity set is constructed within the distributionally robust optimization (DRO) framework. Notably, the proposed residual-based Wasserstein DRO model is demonstrated to satisfy both finite sample guarantee and asymptotic optimality. A linear decision rule (LDR) approximation facilitates a tractable reformulation, and it is shown to incur no loss of optimality for the single-period case. We validate the practical applicability of our model using a dataset from RideAustin, a ride-hailing platform operating in Austin, Texas. We find that the hybrid fleet approach gets the best of both worlds by increasing average revenue using crowd-sourced vehicles and enhancing system robustness using in-house vehicles. Interestingly, we show that a “sweet-spot” may exist as an optimal ratio between the number of in-house and crowd-sourced vehicles that maximizes the overall revenue.