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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".