TY - GEN
T1 - Empirical Analysis of Demand Patterns and Availability in Free-Floating Carsharing Systems
AU - Hardt, Cornelius
AU - Bogenberger, Klaus
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/7
Y1 - 2018/12/7
N2 - Carsharing (CS) services enable users not to have to depend on a privately owned vehicle by supplying cars for spontaneous rental. Optimized service, higher system availability and a more efficient urban transportation system can be obtained by analyzing a wide usage behavior and diurnal patterns within such a system. In order to gain a deeper understanding of the spatio-temporal behavior of such a service, it is vital to distinguish different demand patterns occurring within urban areas. We therefore analyze the demand for carsharing in the city of Munich, Germany. Based on data of a free-floating carsharing (FFCS) service provider we analyze rentals, drop-offs, and resulting availability of vehicles within different urban areas as well as develop diurnal demand patterns and cluster these by incorporating an incremental cross-correlation clustering. The compiled pattern clusters reveal notable distinctions in terms of demand, returns, and vehicle availability between the examined areas. By recognizing and analyzing these distinctions, FFCS will be able to adapt to new business areas, service operation, and pricing strategies in order to optimize their service.
AB - Carsharing (CS) services enable users not to have to depend on a privately owned vehicle by supplying cars for spontaneous rental. Optimized service, higher system availability and a more efficient urban transportation system can be obtained by analyzing a wide usage behavior and diurnal patterns within such a system. In order to gain a deeper understanding of the spatio-temporal behavior of such a service, it is vital to distinguish different demand patterns occurring within urban areas. We therefore analyze the demand for carsharing in the city of Munich, Germany. Based on data of a free-floating carsharing (FFCS) service provider we analyze rentals, drop-offs, and resulting availability of vehicles within different urban areas as well as develop diurnal demand patterns and cluster these by incorporating an incremental cross-correlation clustering. The compiled pattern clusters reveal notable distinctions in terms of demand, returns, and vehicle availability between the examined areas. By recognizing and analyzing these distinctions, FFCS will be able to adapt to new business areas, service operation, and pricing strategies in order to optimize their service.
KW - carsharing
KW - demand patterns
KW - spacial analysis
KW - usage behavior
UR - http://www.scopus.com/inward/record.url?scp=85060464245&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2018.8569700
DO - 10.1109/ITSC.2018.8569700
M3 - Conference contribution
AN - SCOPUS:85060464245
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1186
EP - 1193
BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Y2 - 4 November 2018 through 7 November 2018
ER -
