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Carsharing in Switzerland: identifying new markets by predicting membership based on data on supply and demand

Author

Listed:
  • Maria Juschten

    (University of Natural Resources and Life Sciences)

  • Timo Ohnmacht

    (University of Applied, Sciences and Arts)

  • Vu Thi Thao

    (University of Applied, Sciences and Arts)

  • Regine Gerike

    (University of Dresden)

  • Reinhard Hössinger

    (University of Natural Resources and Life Sciences)

Abstract

This study examines the relationship between the supply and demand variables that determine the probability of individuals joining a carsharing organization, using data from the Swiss National Travel Survey 2010 (demand) and the carsharing company Mobility (supply), located in Switzerland. Carsharing membership as reported in the Swiss National Travel Survey was used as the response variable for a discrete choice model (binominal logit), indicating that supply and demand variables are both significantly related to membership in carsharing organizations. By focusing on the relationships between the supply and demand factors, together with GIS mapping tools, potential markets for the expansion of carsharing in Swiss conurbations where demand is higher than expected based on the supply attributes (deviator groups) are identified. These markets are located in conurbations, are close to railways, support multi- and inter-modal life-styles and are suitable for the conventional round-trip model of carsharing. The results also suggest that individual preferences for transportation policies that aim at implementing mobility pricing for both public transport and car travel are positively related to carsharing membership. Therefore, we conclude that carsharing is not only an economic choice, but also a decision driven by a life-style involving sustainable mobility as a way of reducing infrastructure expansion.

Suggested Citation

  • Maria Juschten & Timo Ohnmacht & Vu Thi Thao & Regine Gerike & Reinhard Hössinger, 2019. "Carsharing in Switzerland: identifying new markets by predicting membership based on data on supply and demand," Transportation, Springer, vol. 46(4), pages 1171-1194, August.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:4:d:10.1007_s11116-017-9818-7
    DOI: 10.1007/s11116-017-9818-7
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    References listed on IDEAS

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    Cited by:

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    4. Golalikhani, Masoud & Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando & Antunes, António Pais, 2021. "Carsharing: A review of academic literature and business practices toward an integrated decision-support framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
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    6. Tiansheng Yang & Ken Peattie & Jean-Paul Skeete & Nicole Koenig-Lewis, 2024. "Navigating Transitions: How Electric Vehicle Sharing Is Shaping Sustainable Mobility in Chinese Cities," Sustainability, MDPI, vol. 16(19), pages 1-21, September.
    7. Thurner, Thomas & Fursov, Konstantin & Nefedova, Alena, 2022. "Early adopters of new transportation technologies: Attitudes of Russia’s population towards car sharing, the electric car and autonomous driving," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 403-417.
    8. Wiedemann, Nina & Xin, Yanan & Medici, Vasco & Nespoli, Lorenzo & Suel, Esra & Raubal, Martin, 2024. "Vehicle-to-grid for car sharing - A simulation study for 2030," Applied Energy, Elsevier, vol. 372(C).
    9. Lucia Rotaris & Marko Bumbulovic, 2020. "Carsharing: Business models, and role of the decision maker," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 0(1), pages 63-94.

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