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

Yingheng Zhang, Haojie Li () and Gang Ren
Additional contact information
Yingheng Zhang: Southeast University
Haojie Li: Southeast University
Gang Ren: Southeast University

Transportation, 2025, vol. 52, issue 1, No 8, 215-237

Abstract: 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.

Keywords: Gasoline price elasticity; Behavioral responses; Vehicle miles traveled; Machine learning (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11116-023-10417-w Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:transp:v:52:y:2025:i:1:d:10.1007_s11116-023-10417-w

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/11116/PS2

DOI: 10.1007/s11116-023-10417-w

Access Statistics for this article

Transportation is currently edited by Kay W. Axhausen

More articles in Transportation from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().