Hybrid Choice Modeling of New Technologies for Car Choice in Canada. Transportation Research Record
Bibliographic record
Abstract
In the last decade, a new trend in discrete choice modeling has emerged in which psychological factors are explicitly incorporated in order to enhance the behavioral representation of the choice process. In this context, Hybrid Models expand on standard choice models by including attitudes and perceptions as latent variables. The complete model is composed of a group of structural equations describing the latent variables in terms of observable exogenous variables, and a group of measurement relationships linking latent variables to certain observable indicators. Although the estimation of Hybrid Models requires the evaluation of complex multi-dimensional integrals, simulated maximum likelihood is implemented in order to solve the integrated multi-equation model. In this paper we study empirically the application of Hybrid Choice Modeling to data from a survey conducted by the EMRG (Simon Fraser University, 2002-2003) of virtual personal vehicle choices made by Canadian consumers when faced with technological innovations. The survey also includes a complete list of indicators, allowing us to apply a Hybrid Choice Model formulation. We conclude that Hybrid Choice is genuinely capable of adapting to practical situations by including latent variables among the set of explanatory variables. Incorporating perceptions and attitudes in this way leads to more realistic models and gives a better description of the profile of consumers and their adoption of new private transportation technologies. 2
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".