Static Pricing Optimization in Shared Mobility Systems Under the Consideration of Network Effects

Over the last decades, shared mobility systems have become an integral part of the inner-city mobility offer – a prominent example is car sharing. In fact, this work has been motivated by the insights we gained in close collaboration with Share Now, Europe's largest car sharing provider. In car sharing as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of controlling the complex interactions between demand and supply in order to increase profitability. Practice mostly applies static price differentiation according to a rental's spatial origin and the time of day. In research, however, such approaches have not been considered in detail yet. In this paper, we consider the static origin-based, profit-maximizing pricing problem for shared mobility systems. The problem is characterized by the determination of spatially and temporally differentiated minute prices, by the prevalence of spatio-temporal network effects, and by other practice-relevant aspects, such as a limited fleet size. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove it to be NP-hard. We propose a scalable heuristic solution approach that combines the computational benefits of problem decomposition in a rolling horizon fashion with a value function approximation technique adapted from approximate dynamic programming in order to incorporate future spatio-temporal network effects. An extensive computational study demonstrates the benefits of capturing such effects in pricing in general, as well as our value function approximation's ability to anticipate them precisely. Moreover, in a case study based on Share Now data from Florence in Italy, we demonstrate potential profit increases of around 9% compared to the de facto industry standard of constant uniform minute prices.