Art der Publikation: Beitrag in Zeitschrift

Differentiated Pricing of Shared Mobility Systems Considering Network Effects

Autor(en):

Soppert, M.; Steinhardt, C.; Müller, C.; Gönsch, J.

Titel der Zeitschrift:

Transportation Science

Jahrgang (Veröffentlichung):

56 (2022)

Heftnummer:

5

Seiten:

1111-1408

Digital Object Identifier (DOI):

doi:10.1287/trsc.2022.1131

Link zum Volltext:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3745001

Zitation:

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Kurzfassung

Over the last decades, shared mobility systems have become an integral part of inner-city mobility. Modern systems allow one-way rentals, i.e. customers can drop off the vehicle at a different location to where they began their trip. A prominent example is car sharing. Indeed, this work was motivated by the insight we gained in collaborating closely with Europe's largest car sharing provider, Share Now. In car sharing, as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of increasing profit while challenged by limited vehicle supply and asymmetric demand across time and space. Thus, in practice, providers increasingly use minute pricing that is differentiated according to where a rental originates, i.e., considering its location and the time of day. In research, however, such approaches have not been considered yet. In this paper, we therefore introduce the corresponding origin-based differentiated, profit-maximizing pricing problem for shared mobility systems. The problem is to determine spatially and temporally differentiated minute prices, taking network effects on the supply side as well as several practice relevant aspects into account. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove that it is NP-hard. For its solution, we propose a temporal decomposition approach based on approximate dynamic programming. The approach integrates a value function approximation to incorporate future profits and account for network effects. Extensive computational experiments demonstrate the benefits of capturing such effects in pricing generally, as well as showing our value function approximation's ability to anticipate them precisely. Further, in a case study based on Share Now data from Florence in Italy, we observe profit increases of around 9% compared to constant uniform minute prices, which are still the de facto industry standard.