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User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks

Author

Listed:
  • Fabio Luis Marques dos Santos

    (Joint Research Centre (JRC), European Commission, 21027 Ispra, Italy)

  • Paolo Tecchio

    (Joint Research Centre (JRC), European Commission, 21027 Ispra, Italy)

  • Fulvio Ardente

    (Joint Research Centre (JRC), European Commission, 21027 Ispra, Italy)

  • Ferenc Pekár

    (Joint Research Centre (JRC), European Commission, 21027 Ispra, Italy)

Abstract

This paper presents an artificial neural network (ANN) model that simulates user’s choice of electric or internal combustion engine automotive vehicles based on basic vehicle attributes (purchase price, range, operating cost, taxes due to emissions, time to refuel/recharge and vehicle price depreciation), with the objective of analyzing user behavior and creating a model that can be used to support policymaking. The ANN was trained using stated preference data from a survey carried out in six European countries, taking into account petrol, diesel and battery electric automotive vehicle attributes. Model results show that the electric vehicle parameters (especially purchase cost, range and recharge times), as well as the purchase cost of internal combustion engine vehicles, have the most influence on consumers’ vehicle choices. A graphical interface was created for the model, to make it easier to understand the interactions between different attributes and their impacts on consumer choices and thus help policy decisions.

Suggested Citation

  • Fabio Luis Marques dos Santos & Paolo Tecchio & Fulvio Ardente & Ferenc Pekár, 2021. "User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:585-:d:477615
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    References listed on IDEAS

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