Empirical Data Analysis of Free-Floating Carsharing Systems

Carsharing has over the last years become an important addition to existing mobility services. Today different Carsharing systems are installed and operated in many cities around the world. For an efficient and economic operation of any Carsharing system, the identification of customer demand is essential. This demand is investigated within the presented research by analyzing real booking data of a German Free-Floating Carsharing system in two cities. The objective of this paper is to identify the Carsharing customers’ usage and general factors that have an influence on the use for Carsharing. Different temporal and spatial distributions of bookings are calculated and illustrated. A cluster analysis is applied identifying groups of time periods with similar spatial booking frequencies and showing asymmetries in the spatiotemporal distribution of vehicle supply and demand. Influences on the demand can either be short-term or long-term. The paper proofs that changes of weather conditions have a short-term influence. Users of Free-Floating Carsharing react statistically significant to weather changes. Furthermore the application of a linear regression model shows that socio-demographic data can be used for long-term demand and business district predictions.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AP020 Emerging and Innovative Public Transport and Technologies.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Schmöller, Stefan
  • Weikl, Simone
  • Müller, Johannes
  • Bogenberger, Klaus
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 17p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Cluster analysis; Data analysis; Linear regression analysis; Mobility; Mode choice; Travel demand; Vehicle sharing; Weather conditions
• Uncontrolled Terms: Spatiotemporal data
• Geographic Terms: Germany
• Subject Areas: Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01519783
• Record Type: Publication
• Report/Paper Numbers: 14-4410
• Files: TRIS, TRB, ATRI
• Created Date: Mar 26 2014 10:11AM