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.

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
38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017
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Rotterdam School of Management (RSM), Erasmus University

Kahlen, M.T, Lee, T.Y. (Thomas Y.), Ketter, W, & Gupta, A. (Alok). (2018). Optimal Prepositioning and Fleet Sizing to Maximize Profits for One-Way Transportation Companies. In ICIS 2017: Transforming Society with Digital Innovation. Retrieved from