Moving in time and space – Location intelligence for carsharing decision support

Willing C, Klemmer K, Brandt T, Neumann D


Abstract
In this paper we develop a spatial decision support system that assists free-floating carsharing providers in countering imbalances between vehicle supply and customer demand in existing business areas and reduces the risk of imbalance when expanding the carsharing business to a new city. For this purpose, we analyze rental data of a major carsharing provider in the city of Amsterdam in combination with points of interest (POIs). The spatio-temporal demand variations are used to develop pricing zones for existing business areas. We then apply the influence of POIs derived from carsharing usage in Amsterdam in order to predict carsharing demand in the city of Berlin. The results indicate that predicted and actual usage patterns are very similar. Hence, our approach can be used to define new business areas when expanding to new cities to include high demand areas and exclude low demand areas, thereby reducing the risk of supply-demand imbalance.

Keywords
Carsharing; Spatial analytics; Location-based services; Spatial decision support system



Publication type
Research article (journal)

Peer reviewed
Yes

Publication status
Published

Year
2017

Journal
Decision Support Systems

Volume
99

Start page
75

End page
85

Language
English

ISSN
0167-9236

DOI

Full text