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Data-driven exploration of heterogeneous gasoline price elasticities using generalized random forests

Author

Listed:
  • Yingheng Zhang

    (Southeast University
    Jiangsu Key Laboratory of Urban ITS
    Urban Traffic Technologies)

  • Haojie Li

    (Southeast University
    Jiangsu Key Laboratory of Urban ITS
    Urban Traffic Technologies)

  • Gang Ren

    (Southeast University
    Jiangsu Key Laboratory of Urban ITS
    Urban Traffic Technologies)

Abstract

Gasoline price elasticities are of central importance for measuring road user responses to price changes. There are a growing number of studies placing their focuses on the underlying heterogeneity due to its practical implications. This paper explores the heterogeneity in household vehicle use responses (measured by vehicle miles traveled) to the gasoline price using the generalized random forest (GRF) method, which is able to discover heterogeneities in a data-driven way. A simulation study based on semi-synthetic datasets constructed from the US 2017 National Household Travel Survey indicates that GRF performs well in estimating gasoline price elasticities and uncovering the source of the heterogeneity. Our empirical study finds a negative average price elasticity of − 0.386, with systematic heterogeneities across household and location characteristics. Based on these findings, policymaking could be performed in a more precise way, which is expected to reduce inequalities and unfairness. Regarding the implementation of GRF, the modeling procedure adopted in this paper seems practical.

Suggested Citation

  • Yingheng Zhang & Haojie Li & Gang Ren, 2025. "Data-driven exploration of heterogeneous gasoline price elasticities using generalized random forests," Transportation, Springer, vol. 52(1), pages 215-237, February.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:1:d:10.1007_s11116-023-10417-w
    DOI: 10.1007/s11116-023-10417-w
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