Dynamic Pricing for Shared Mobility Systems Based on Idle Time Data

Forthcoming in OR Spectrum, doi: 10.1007/s00291-023-00732-0

39 Pages Posted: 27 Aug 2023 Last revised: 30 Apr 2024

See all articles by Christian Müller

Christian Müller

University of Duisburg-Essen - Mercator School of Management

Jochen Gönsch

University of Duisburg-Essen - Mercator School of Management

Matthias Soppert

University of the Bundeswehr Munich

Claudius Steinhardt

University of the Bundeswehr Munich

Date Written: August 24, 2023

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 either based 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 spatiotemporal granularity. 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.

Suggested Citation

Müller, Christian and Gönsch, Jochen and Soppert, Matthias and Steinhardt, Claudius, Dynamic Pricing for Shared Mobility Systems Based on Idle Time Data (August 24, 2023). Forthcoming in OR Spectrum, doi: 10.1007/s00291-023-00732-0, Available at SSRN: https://ssrn.com/abstract=4550633

Christian Müller

University of Duisburg-Essen - Mercator School of Management ( email )

Lotharstraße 65
Duisburg, Nordrhein-Westfalen 47057
Germany

Jochen Gönsch (Contact Author)

University of Duisburg-Essen - Mercator School of Management ( email )

Lotharstraße 65
Duisburg, Nordrhein-Westfalen 47057
Germany
+49 203 379 - 2777 (Phone)
+49 203 379 - 1760 (Fax)

HOME PAGE: http://udue.de/goensch

Matthias Soppert

University of the Bundeswehr Munich ( email )

Werner-Heisenberg-Weg 39
Neubiberg, 85577
Germany

Claudius Steinhardt

University of the Bundeswehr Munich ( email )

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