Exploratory data science for discovery and ex‐ante assessment of operational policies: Insights from vehicle sharing

Abstract: The proliferation of mobile devices and the emergence of the Internet of Things are leading to an unprecedented availability of operational data. In this article, we study how leveraging this data in conjunction with data science methods can help researchers and practitioners in the development and evaluation of new operational policies. Specifically, we introduce a two-stage framework for exploratory data science consisting of a policy identification stage and an ex-ante policy assessment stage. We apply the framework to the context of free-floating carsharing—a novel mobility service that is an example of datarich smart city services. Through data exploration, we identify a novel preventive user-based relocation policy and provide an ex-ante assessment regarding the feasibility of its implementation. We discuss practical implications of our approach and results for shared-mobility providers as well as the relationship between data science and operations management research

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Journal of operations management. - 67, 3 (2020) , 307-328, ISSN: 1873-1317

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Freiburg
• (who): Universität
• (when): 2020
• Creator: Brandt, Tobias ; Dlugosch, Oliver

Further information

• DOI: 10.1002/joom.1125
• URN: urn:nbn:de:bsz:25-freidok-1734740
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:30 AM CEST