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Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions

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  • Aurélie Glerum

    (École Polytechnique Fédérale de Lausanne (EPFL), School of Architecture, Civil and Environmental Engineering (ENAC), Transport and Mobility Laboratory (TRANSP-OR), Lausanne, Switzerland CH-1015)

  • Lidija Stankovikj

    (École Polytechnique Fédérale de Lausanne (EPFL), School of Architecture, Civil and Environmental Engineering (ENAC), Transport and Mobility Laboratory (TRANSP-OR), Lausanne, Switzerland CH-1015)

  • Michaël Thémans

    (École Polytechnique Fédérale de Lausanne (EPFL), Vice-Presidency for Technology Transfer (VPIV), Transportation Center (TRACE), Lausanne, Switzerland CH-1015)

  • Michel Bierlaire

    (École Polytechnique Fédérale de Lausanne (EPFL), School of Architecture, Civil and Environmental Engineering (ENAC), Transport and Mobility Laboratory (TRANSP-OR), Lausanne, Switzerland CH-1015)

Abstract

In the context of the arrival of electric vehicles on the car market, new mathematical models are needed to understand and predict the impact on the market shares. This research provides a comprehensive methodology to forecast the demand of a technology that is not widespread yet, such as electric cars. It aims at providing contributions regarding three issues related to the prediction of the demand for electric vehicles: survey design, model estimation, and forecasting. We develop a stated preferences (SP) survey with personalized choice situations involving standard gasoline/diesel cars and electric cars. We specify a hybrid choice model accounting for attitudes toward leasing contracts or practical aspects of a car in the decision-making process. A forecasting analysis based on the collected SP data and additional market information is performed to evaluate the future demand for electric cars.

Suggested Citation

  • Aurélie Glerum & Lidija Stankovikj & Michaël Thémans & Michel Bierlaire, 2014. "Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions," Transportation Science, INFORMS, vol. 48(4), pages 483-499, November.
    • Handle: RePEc:inm:ortrsc:v:48:y:2014:i:4:p:483-499
    DOI: 10.1287/trsc.2013.0487
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    References listed on IDEAS

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