What Type of Vehicles Do Households Own? A Joint Model for Vehicle Body, Vintage, Fuel, and Technology Types
Bibliographic record
Abstract
Abstract Households’ vehicle fleet composition has important policy implications in the area of transport-related energy consumption and emissions. With the recent development in different alternative fuel vehicles (AFVs) such as hybrid and electric vehicles and advanced technology features in the vehicle, the choice dimensions during vehicle purchase are not just limited to vehicle body and age. Households may also consider fuel and technology types. Therefore, this study focuses to investigate households’ vehicle type choices, specifically vehicle body, vintage, fuel, and technology types utilizing a survey conducted in British Columbia, Canada. A joint multinomial probit model has been developed that accommodates error correlations across alternatives among the different choice dimensions. The model results confirm significant correlations among the unobserved components. For instance, a significant positive correlation exists between alternative fuel vehicles and vehicles with advanced technology. The study also investigates the effects of households’ historical experiences such as historical vehicle fleet composition, and exposure to technology in daily life and vehicles. Historically owning AFVs and advanced technology in vehicles are found to have positive effects on the future preference for vehicles with advanced technology. Transit users and bikers show an inclination towards AFVs, which indicates a need for closer monitoring of the early adopters. The findings of the study provide insights towards targeted marketing to equitably promote the ownership of more sustainable, safer, and fuel-efficient vehicles among diverse age and income groups. Furthermore, the results inform land use and transportation policies to influence vehicle type choices for reducing fuel consumption and emissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".