Optimal Prepositioning and Fleet Sizing to Maximize Profits for One-Way Transportation Companies

Micha Kahlen, Thomas Y. Lee, Wolfgang Ketter, Alok Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Companies providing mobility solutions, such as Uber, Lyft and a host of short-term car rental companies, can generate value from information technology to track their vehicles location. Real-time decision making to act on this information is critical for the success of one-way mobility companies. We show how these companies can predict vehicle demand with high accuracy for vehicles across time and urban areas. We validate this model by tracking the movement and transactions of 1,100 vehicles from the carsharing service Car2Go in Berlin. With our model they could preposition vehicles to increase service levels with a smaller fleet. The accuracy of the model to predict demand area and times is a key contribution to urban mobility systems. Prepositioning vehicles based on expected demand and supply as modeled in our paper are vital to the business models of emerging transportation network companies like Uber and will be indispensable for autonomous vehicles.

Original languageEnglish (US)
Title of host publicationICIS 2017
Subtitle of host publicationTransforming Society with Digital Innovation
PublisherAssociation for Information Systems
ISBN (Print)9780996683159
StatePublished - 2018
Event38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017 - Seoul, Korea, Republic of
Duration: Dec 10 2017Dec 13 2017

Publication series

NameICIS 2017: Transforming Society with Digital Innovation

Other

Other38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017
Country/TerritoryKorea, Republic of
CitySeoul
Period12/10/1712/13/17

Keywords

  • Business value of IS/value of IS
  • Complex adaptive systems
  • Data mining
  • Decision Support Systems (DSS)
  • Forecasts
  • Green IT/IS
  • ICT artifact
  • Implications
  • Predictive modeling

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