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An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

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  • Johannes Müller

    (Institute for Traffic, Transport and Regional Planning, University of the Federal Armed Forces Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany)

  • Gonçalo Homem de Almeida Correia

    (Department of Transport & Planning, TU Delft, Stevinweg 1, 2628 CN Delft, The Netherlands)

  • Klaus Bogenberger

    (Institute for Traffic, Transport and Regional Planning, University of the Federal Armed Forces Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany)

Abstract

When the first free-floating carsharing operators launched their business, they did not know if it would be profitable. They often started in highly populated cities without performing extensive target group analysis, and were less concerned about fleet management. Usually, there are two main datasets that can be used to find areas that would have a high demand for free-floating carsharing: booking data, for measuring the actual demand; and land use and census data for describing the activities performed in different areas in a city. In this paper, we aim to use this information to help predict the demand of free-floating carsharing systems. We use booking data provided by DriveNow for Berlin in 2014 and contextual information about the type of activity each neighborhood has. Using Berlin as a case study, we apply a negative binomial statistical model to explain the number of bookings. From the results, we conclude that free-floating carsharing is predominantly successful in areas with more affluent citizens who are open to trying new and sustainable technologies. Other important determinants that result in a high number of carsharing bookings are the area’s centrality and parking lot availability. The statistical model for Berlin was then transferred to Munich and Cologne, two other cities in Germany with similar population sizes. A comparison between the estimated demand categories and actual bookings shows satisfying results, but also non-negligible local conditions influencing the spatial demand for bookings.

Suggested Citation

  • Johannes Müller & Gonçalo Homem de Almeida Correia & Klaus Bogenberger, 2017. "An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1290-:d:105691
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    References listed on IDEAS

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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).
    5. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    6. Soppert, Matthias & Steinhardt, Claudius & Müller, Christian & Gönsch, Jochen & Bhogale, Prasanna M., 2023. "Matching functions for free-floating shared mobility system optimization to capture maximum walking distances," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1194-1214.
    7. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.

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