Identifying Usage Profiles of Station-Based Car-Sharing Members Using Cluster Analyses

With the growing usage of the internet, the possibility for shared mobility has risen just as much. Beside ride-sharing, bike-sharing, and shared parking, this applies, especially to car-sharing. Past research activities have often been limited to the economic, ecological, and urban benefits of car-sharing, such as the number of privately owned cars that could be replaced by car-sharing vehicles or the potential to save parking space. These analyses disregard the user’s behavior and patterns of usage. However, to analyze, e.g., future market shares of car-sharing, we first have to evaluate how car-sharing members use car-sharing and what purposes the trips might serve. One such study has been conducted in Germany, however, using free-floating car-sharing data. The focus of research is put on data from a station-based car-sharing provider and what kind of user or usage profiles can be identified. The authors investigated this by performing a cluster analysis using the k-means algorithm. The results indicate that there are five types of station-based car-sharing users and usage respectively. There are commercial users, users who use car-sharing for regular and users who use it for irregular activities. Furthermore, car-sharing vehicles are used to replace a second car and also for long distance travels. These findings are in part consistent with the study on free-floating car-sharing but also show some dissimilarities, as to be expected since the two systems generally serve different purposes.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 23p

Subject/Index Terms

Filing Info

  • Accession Number: 01711089
  • Record Type: Publication
  • Report/Paper Numbers: 19-02785
  • Files: TRIS, TRB, ATRI
  • Created Date: Jul 16 2019 10:31AM