ROBUST ANALYSIS OF DISCRETE CHOICE IN TRANSPORT WITH AN APPLICATION TO ALBERTA CYCLISTS
Bibliographic record
Abstract
In this paper we apply recently-developed nonparametric conditional kernel density estimation to model discrete choice in a transportation context. Our empirical application, for the purpose of demonstrating the technique in a transport context, is to the route choices of cyclists in the Canadian province of Alberta. The empirical analysis employs the nonparametric multivariate kernel with mixed data types (continuous and discrete). This approach permits the inclusion of continuous and discrete variables where the rate of convergence only depends on the number of continuous variables; it allows for interactions between the covariates, which may be important determinants in discrete transport decisions; it allows the estimation of marginal effects without assuming a constant response regardless of the level of the explanatory variable as it would be in a linear model; and the non-parametric estimation does not impose assumptions in the underlying economic behavior such as IIA. Using a nonparametric index-free approach, the data are permitted to model the full range of relationships among variables. In our simple application of cyclist route decisions, the kernel-density-based estimator correctly classifies 76.24 % of decisions in contrast to a correct classification rate of 61.28 % for the probit model and 61.44 % for the logit
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".