@article{35335,
  keywords = {Machine learning, Life events, Travel behavior, Vehicle transaction, Mobility biography, Longitudinal data, SHAP TreeExplainer, Multinomial logit},
  author = {Ling Jin and Alina Azar and Caitlin Brown and Bingrong Sun and Venu Garikapati and Srinath Ravulaparthy and Qianmiao Chen and Alex Sim and Kesheng Wu and Tin Ho and Thomas P Wenzel and C Anna Spurlock},
  title = {What Makes You Hold on to That Old Car? Joint Insights from Machine Learning and Multinomial Logit on Vehicle-level Transaction Decisions},
  abstract = {<p>What makes you hold on that old car? While the vast majority of the 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. 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.</p>},
  year = {2022},
  journal = {Frontiers in Future Transportation - Connected Mobility and Automation},
  month = {06/2022},
  url = {https://www.frontiersin.org/articles/10.3389/ffutr.2022.894654/full?utm_source=F-NTF&utm_medium=EMLX&utm_campaign=PRD_FEOPS_20170000_ARTICLE},
  doi = {10.3389/ffutr.2022.894654},
  language = {eng},
}