Type of Publication: Article in Journal

Dynamic Pricing for Shared Mobility Systems Based on Idle Time Data

Author(s):

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

Title of Journal:

OR Spectrum

Publication Date:

2023

Keywords:

Dynamic pricing · Idle time data · Anticipation · Disaggregated customer choice modeling

Digital Object Identifier (DOI):

doi:10.1007/s00291-023-00732-0

Link to complete version:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4550633

Citation:

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Abstract

In most major cities today, various shared mobility systems such as car or bike sharing
exist. Maintaining these systems is challenging, and, thus, public and private
providers strive to improve operational performance. An important metric which is
regularly recorded and monitored in practice for this purpose is idle time, i.e., the
time a vehicle stands unused between two rentals. Usually, it is available for different
temporal and spatial granularities. At the same time, dynamic pricing has been
shown to be an efficient means for increasing operational performance in shared
mobility systems, but data necessary for traditional dynamic pricing approaches,
like unconstrained demand, is much less available in practice. Thus, dynamic pricing
based on idle time data appears promising and first ideas have been proposed.
However, the existing approaches are based either on simple business rules or on
myopic optimization. In this work, we develop a novel dynamic pricing approach
that determines prices by online optimization and thereby anticipates future profits
through the integration of idle time data. The core idea is quantifying the remaining
profitable time by using idle times. With regard to application in practice, the
developed approach is generic in the sense that different types of readily available
historical idle time data can be seamlessly integrated, meaning data of different
spatio-temporal granularities. In an extensive numerical study, we demonstrate that
the operational performance increases with higher granularity and that the approach
with the highest one outperforms current pricing practice by up to 11% in terms of
profit.