Modeling Households’ First Vehicle Purchase Timing and Vehicle Type Choices
Bibliographic record
Abstract
First vehicle purchase is a crucial decision as it dictates households’ travel behavior which consequently impacts traffic congestion, emissions, and energy consumption. This paper focuses on investigating first vehicle purchase timing and type choices. The timing of the first vehicle purchase is investigated using a hazard-based duration model. This method accommodates the continuous time dimension of households’ car-free state and transitions to the car ownership state through its termination. Vehicle type choice considers three choice dimensions: body type, vintage type, and presence of technology. A joint discrete choice model is developed for vehicle types which captures the correlation between different choice dimensions. The timing and type choice models are developed in a nested structure using the logsum parameters. The results confirm the presence of significant correlations between vehicle type choices. The timing model also retains a statistically significant logsum from the vehicle type choice model. The study confirms that life-cycle events and longer-term changes, built-environment characteristics of the residence, mobility tool ownership, and socio-demographic attributes are significant determinants of first vehicle purchase decisions. The birth of a child, residential relocation, and the addition of a job are likely to accelerate the first vehicle purchase whereas the loss of a job has the opposite effect. Urban dwellers are likely to take a longer duration to transition from being car-free to owning a car compared with others. The findings provide important insights into the factors that delay the first vehicle purchase decisions and encourage the ownership of more efficient vehicles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".