Data-Driven Competitor-Aware Positioning in On-Demand Vehicle Rental Networks

Transportation Science, Forthcoming

51 Pages Posted: 1 Oct 2021

See all articles by Karsten Schroer

Karsten Schroer

University of Cologne - Cologne Institute for Information Systems (CIIS); University of Cologne - Institute of Energy Economics

Wolfgang Ketter

University of Cologne - Faculty of Management, Economics and Social Sciences; Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management; Erasmus Research Institute of Management (ERIM)

Thomas Lee

University of California, Berkeley

Alok Gupta

University of Minnesota - Twin Cities - Carlson School of Management

Micha Kahlen

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM)

Date Written: September 1, 2021

Abstract

We study a novel operational problem that considers vehicle positioning in on-demand rental networks such as carsharing in the wider context of a competitive market in which users select vehicles based on access. Existing approaches consider networks in isolation; our competitor-aware model takes supply situations of competing networks into account. We combine online machine learning to predict market-level demand and supply with dynamic mixed integer non-linear programming (MINLP). For evaluation we use discrete event simulation based on real-world data from Car2Go and DriveNow. Our model outperforms conventional models that consider the fleet in isolation by a factor of 2 in terms of profit improvements. In the case we study, the highest theoretical profit improvements of 7.5\% are achieved with a dynamic model. Operators of on-demand rental networks can use our model under existing market conditions to build a profitable competitive advantage by optimizing access for consumers without the need for fleet expansion. Model effectiveness increases further in realistic scenarios of fleet expansion and demand growth. Our model accommodates rising demand, defends against competitors' fleet expansion and enhances the profitability of own fleet expansions.

Keywords: machine learning, online optimization, optimal positioning, sharing economy, Car2Go

Suggested Citation

Schroer, Karsten and Ketter, Wolfgang and Lee, Thomas and Gupta, Alok and Kahlen, Micha, Data-Driven Competitor-Aware Positioning in On-Demand Vehicle Rental Networks (September 1, 2021). Transportation Science, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3915466 or http://dx.doi.org/10.2139/ssrn.3915466

Karsten Schroer (Contact Author)

University of Cologne - Cologne Institute for Information Systems (CIIS) ( email )

Pohligstr. 1
Cologne, D-50969
Germany

University of Cologne - Institute of Energy Economics ( email )

Alte Wagenfabrik
Vogelsanger Strasse 321a
Cologne, 50827
Germany

Wolfgang Ketter

University of Cologne - Faculty of Management, Economics and Social Sciences ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

HOME PAGE: http://is3.uni-koeln.de

Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management ( email )

RSM Erasmus University
PO Box 1738
3000 DR Rotterdam
Netherlands

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

HOME PAGE: http://www.rsm.nl/energy

Thomas Lee

University of California, Berkeley ( email )

310 Barrows Hall
Berkeley, CA 94720
United States

Alok Gupta

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

Micha Kahlen

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM) ( email )

P.O. Box 1738
Room T08-21
3000 DR Rotterdam, 3000 DR
Netherlands

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