Exploiting the Elicited Confidence Ratings of SP Surveys for Better Estimates of Choice Model Parameters: the Case of Commuting Mode Choices in a Multimodal Transportation System
Bibliographic record
Abstract
This paper presents a robust method of joint revealed preference-stated preference ( RP-SP) choice model that exploits the endogeneity between stated choice and its corresponding certainty indices. The proposed model also accounts for inertia effect, effects of socio-economic variables and heteroskedasticity in the joint RP-SP context. SP scale parameter was parameterized as a function of personal attributes to account for correlations between repeated SP choices. Proposed empirical models investigate commuting mode choice behaviour by using data collected in the Greater Toronto and Hamilton Area (GTHA). The results of empirical models show that capturing endogeneity between SP choice tasks and corresponding elicited confidence ratings improves the efficiency of parameter estimates. It is also found that including inertia effect and socio-economic variables improves model’s goodness-of-fit values. However, no evidence is found on the role of endogeneity between SP choices and corresponding elicited confidence ratings on model’s goodness-of-fit measures.
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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 | 0.012 | 0.072 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".