Type Choice Behavior of Alternative Fuel Vehicles: A Latent Class Model Approach
Bibliographic record
Abstract
This study presents the findings of modeling alternative fuel vehicle type choice behavior in the case of a hypothetical scenario of 100% increase in gas prices in Halifax, Canada. A latent class model (LCM) is developed utilizing a stated response component from the Household Mobility and Travel Survey, conducted in Halifax, Canada, in 2012-13. The study considers a comprehensive set of alternative vehicle type choices, including: Diesel Powered Vehicles, Hybrid Electric Vehicles, Plug-In Hybrid electric Vehicles, Plug-In electric Vehicles, and regular gasoline vehicles. The LCM model developed in this paper captures latent heterogeneity among the sample households by developing a flexible latent class allocation model within the LCM framework. In this paper, the LCM model assumes two latent classes, where the classes are defined using socio-demographics, accessibility, and neighborhood characteristics. The model results suggest that considerable heterogeneity exists across the two classes. For instance, presence of children in the household shows a higher probability to choose hybrid electric vehicles in class two. On the other hand, households in class one show a negative relationship. High income households show a lower likelihood of choosing alternative vehicles and exhibit a higher propensity to continue with regular gasoline vehicles. The elasticity effects suggest that significant variation in the magnitude of effects of different variables exist across the two classes, which needs to be addressed within the policies for promoting alternative fuel vehicles as alternate choice for consumers during a sudden increase in gas price.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".