What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions: Article No. 894654

Ling Jin, Alina Lazar, Caitlin Brown, Bingrong Sun, Venu Garikapati, Srinath Ravulaparthy, Qianmiao Chen, Alexander Sim, Kesheng Wu, Tin Ho, Thomas Wenzel, C. Spurlock

Research output: Contribution to journalArticlepeer-review

3 Scopus Citations

Abstract

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle's attributes, 2) mediated by households' concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.
Original languageAmerican English
Number of pages18
JournalFrontiers in Future Transportation
Volume3
DOIs
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5400-82977

Keywords

  • life events
  • machine learning
  • mobility biography
  • travel behavior
  • vehicle transaction

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