Modeling Air Traveler Choice Behavior in Southern Ontario Market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper reports on a passenger survey conducted in the passenger holdrooms of Toronto Pearson Airport. This survey targeted southern Ontario residents traveling non-stop to a selected set of U.S. destinations. Eligible, participating passengers were first asked a series of background questions. Then, they were asked to evaluate a series of choices of airline offerings, based on their current itinerary. Their choices were to remain with their current airline, switch to another local airline, or switch to an airline operating out of a remote airport. Price and schedule were varied for each choice. The data from this stated-preference survey were then used to estimate discrete-choice random utility models (one for business and one for non-business travelers). A nested-logit form was used, along with a normally distributed random coefficient to account for correlations between the choices for each passenger. Initial results from the model estimation are included. For the most part, passenger behavior is consistent with expectations. Further work on the model estimation is required. The information gleaned from this study could be used to estimate passenger acceptance of a proposed new airport in the Toronto area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it