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Evaluating the influence of carsharing stations’ location on potential membership: a Swiss case study

Author

Listed:
  • Francesco Ciari

    (ETH Zürich, IVT, Institute for Transport Planning and Systems)

  • Claude Weis

    (TransOptima GmbH)

  • Milos Balac

    (ETH Zürich, IVT, Institute for Transport Planning and Systems)

Abstract

Carsharing as a mode, in any of its different forms, has the peculiarity of being accessible only to members. The research presented in this paper focuses on round-trip-based carsharing, where vehicles are parked at fixed locations, called in this paper stations. Round-trip-based carsharing has been the first successful form of carsharing, and is still the most diffused one in many countries. Several studies looked at the potential of such a system in various countries but the link between spatial distribution of the stations and potential membership has not been done yet. This paper looks at this question while trying to address other limitations of the existing literature on carsharing potential. The research has two parts. In the first part, a binary logistic model of round-trip based carsharing membership in Switzerland is estimated. The model is based on a large RP dataset (the Swiss national travel diaries survey) where information on carsharing membership is collected. This provides a representative and non-biased sample. The model takes into account accessibility to carsharing at local level introducing a dependency between potential membership and actual availability of the service. The model is then run on a synthetic population representing the whole Swiss population—created based on full census data—and then validated against actual membership data of the Swiss carsharing operator Mobility. It is shown that the model estimated is able to reproduce fairly well the spatial distribution of carsharing members in Switzerland. The second part is aimed at showing that the location of stations actually impacts potential membership. To this purpose an approach to solve the problem of the optimal location of carsharing stations is proposed, where the model estimated in the first part is used as objective function of an optimization algorithm. The region surrounding the city of Zurich is taken as case study for this approach. The proposed technique is suitable to find new constellations of stations so that the number of carsharing members is incremented and can also be seen as an innovative instrument which can help in the planning of carsharing station networks.

Suggested Citation

  • Francesco Ciari & Claude Weis & Milos Balac, 2016. "Evaluating the influence of carsharing stations’ location on potential membership: a Swiss case study," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 345-369, August.
    • Handle: RePEc:spr:eurjtl:v:5:y:2016:i:3:d:10.1007_s13676-015-0076-6
    DOI: 10.1007/s13676-015-0076-6
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    References listed on IDEAS

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    1. Trevor Hale & Christopher Moberg, 2003. "Location Science Research: A Review," Annals of Operations Research, Springer, vol. 123(1), pages 21-35, October.
    2. Prettenthaler, Franz E. & Steininger, Karl W., 1999. "From ownership to service use lifestyle: the potential of car sharing," Ecological Economics, Elsevier, vol. 28(3), pages 443-453, March.
    3. Correia, Gonçalo Homem de Almeida & Antunes, António Pais, 2012. "Optimization approach to depot location and trip selection in one-way carsharing systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 233-247.
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    Citations

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    Cited by:

    1. Zhou, Tianli & Fields, Evan & Osorio, Carolina, 2023. "A data-driven discrete simulation-based optimization algorithm for car-sharing service design," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    2. Sweet, Matthias N. & Scott, Darren M., 2021. "Shared mobility adoption from 2016 to 2018 in the Greater Toronto and Hamilton Area: Demographic or geographic diffusion?," Journal of Transport Geography, Elsevier, vol. 96(C).
    3. 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.
    4. Wenxiang Li & Ye Li & Jing Fan & Haopeng Deng, 2017. "Siting of Carsharing Stations Based on Spatial Multi-Criteria Evaluation: A Case Study of Shanghai EVCARD," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
    5. Weibo Li & Maria Kamargianni, 2020. "Steering short-term demand for car-sharing: a mode choice and policy impact analysis by trip distance," Transportation, Springer, vol. 47(5), pages 2233-2265, October.

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