In free float: Developing Business Analytics support for carsharing providers

• A novel Business Analytics method for carsharing providers is proposed.
• Method uses points of interest to explain spatial variation of carsharing demand.
• Data set includes half a million rentals and 180,000 points of interest in Berlin.
• Predicted and actual demand show strong similarities.
• Method is validated using a real-world expansion of carsharing operation area.

As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator׳s business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities.