Practicable solution approaches for differentiated pricing of vehicle sharing systems

Abstract Vehicle sharing systems have become increasingly popular. However, one-way vehicle sharing system providers face a major challenge. The uneven distribution of vehicles across locations caused by the uneven nature of the demand patterns poses a problem, since there are accumulations of vehicles where the demand is low. This challenge can be solved with an appropriate pricing approach that creates incentives for user-based relocation by considering supply-side network effects. While the literature mostly focuses on trip-based pricing, we were inspired by the majority of car sharing providers who use origin-based minute pricing that differentiates based on the origins of rentals, such as Share Now. Therefore, we develop two different and practicable solution approaches to determine spatially and temporally differentiated origin-based minute prices that take into account supply-side network effects. The first solution approach does not differentiate between rentals and demand and calculates continuous prices for every period and location. The second solution approach determines the vehicle distribution for each period and then calculates the optimal prices for each period backwards. Extensive computational experiments show that our solution approaches anticipate supply-side network effects and thus generate a near-optimal profit in less computational time compared to more complex benchmarks from the literature. In a sensitivity analysis we additionally show that the results are robust against stochasticity of demand and that the solution approaches perform well for different price sets.