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Driving factors behind station-based car sharing adoption: Discovering distinct user profiles through a latent class cluster analysis

Author

Listed:
  • van der Linden, Hidde
  • Correia, Gonçalo
  • van Oort, Niels
  • Koster, Suze
  • Legêne, Martijn
  • Kroesen, Maarten

Abstract

In light of growing environmental challenges, the need to reconsider how we approach personal transportation is becoming increasingly evident. A shift from a private car-focused mobility system towards a more sustainable and equitable transportation system is needed. Car sharing is considered a means to achieve this, however, its usage and its impact are not entirely understood, as many studies do not consider the motives of individuals to use this alternative, treating the population of users as a homogeneous group. This study aims to reveal distinct car sharing usage profiles to gain a thorough understanding of the various motivates behind car sharing and its relation with travel behaviour. Six user profiles are uncovered using a Latent Class Cluster Analysis (LCCA) based on station-based carsharing data of one company operating in the Netherlands gathered through an online survey (N = 1281). The results show significant diversity in car sharing motives. The identified user groups have different effects on travel behaviour. Environmentally motivated car sharers use the shared car as a complete replacement for their private car, causing a substantial decrease in car ownership and usage. For utilitarian car sharers, and especially formerly carless individuals, the decrease in car ownership is less substantial and even an increase in car use can be observed. Finally, it was found that car sharing is mostly complementary to public transport use. Ways to promote the use of both modes could be explored.

Suggested Citation

  • van der Linden, Hidde & Correia, Gonçalo & van Oort, Niels & Koster, Suze & Legêne, Martijn & Kroesen, Maarten, 2025. "Driving factors behind station-based car sharing adoption: Discovering distinct user profiles through a latent class cluster analysis," Transport Policy, Elsevier, vol. 162(C), pages 232-241.
    • Handle: RePEc:eee:trapol:v:162:y:2025:i:c:p:232-241
    DOI: 10.1016/j.tranpol.2024.12.001
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    References listed on IDEAS

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