Location
260-005, Owen G. Glenn Building
Start Date
12-15-2014
Description
Free-floating carsharing is a young and rapidly expanding market that allows customers to end their rentals anywhere within the business area of the provider. Through this flexibility it complements public transportation and reduces the environmental footprint of the transportation sector. We present a novel data analytics methodology that supports companies – from local start-ups to global players – in maneuvering this constantly growing and changing market environment. Using a large set of rental data, we derive indicators for the attractiveness of certain areas based on points of interest in their vicinity, such as shopping malls, movie theaters, or train stations. In a case study of Berlin we use these indicators to accurately identify promising regions for an expansion of the business area. However, the methodology introduced in this paper can also improve operational decisions of the service provider and is applicable to a wide range of other location-based services.
Recommended Citation
Wagner, Sebastian; Brandt, Tobias; Kleinknecht, Marc; and Neumann, Dirk, "In Free-Float: How Decision Analytics Paves the Way for the Carsharing Revolution" (2014). ICIS 2014 Proceedings. 7.
https://aisel.aisnet.org/icis2014/proceedings/DecisionAnalytics/7
In Free-Float: How Decision Analytics Paves the Way for the Carsharing Revolution
260-005, Owen G. Glenn Building
Free-floating carsharing is a young and rapidly expanding market that allows customers to end their rentals anywhere within the business area of the provider. Through this flexibility it complements public transportation and reduces the environmental footprint of the transportation sector. We present a novel data analytics methodology that supports companies – from local start-ups to global players – in maneuvering this constantly growing and changing market environment. Using a large set of rental data, we derive indicators for the attractiveness of certain areas based on points of interest in their vicinity, such as shopping malls, movie theaters, or train stations. In a case study of Berlin we use these indicators to accurately identify promising regions for an expansion of the business area. However, the methodology introduced in this paper can also improve operational decisions of the service provider and is applicable to a wide range of other location-based services.