Paper Info
Title
Moving in time and space - Location intelligence for carsharing decision support.
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.
Year
DOI
Venue
2017
10.1016/j.dss.2017.05.005
Decision Support Systems
Keywords
Field
DocType
Carsharing,Spatial analytics,Location-based services,Spatial decision support system
Spatial analysis,Computer science,Decision support system,Location intelligence,Operations research,Spatial decision support system,Knowledge management,Location-based service,Point of interest,Marketing,Renting
Journal
Volume
ISSN
Citations 
99
0167-9236
5
PageRank 
References 
Authors
0.55
10
4
Name
Order
Citations
PageRank
Christoph Willing150.55
Konstantin Klemmer263.26
Tobias Brandt37717.84
Dirk Neumann429437.29