Modeling Home-to-Work Route Choice Decisions Using GPS Data: A Comparison of Two Approaches for Generating Choice Sets
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates home-to-work route choice decisions. The global positioning system is used to obtain 237 routes for a random sample of individuals in Halifax, Canada. Choice sets are generated by two methods: k-shortest paths and a time-geographic construct. With respect to the latter, the potential path area, which considers an individual’s time budget, is used to constrain routes to those that are feasible given the budget. Conditional logit models are estimated for each approach by sampling nine routes randomly from among the alternatives and adding them to the observed route to form the choice set for each individual. The choice set generation approaches are compared with the time-geographic approach showing superior fit to the data. Both characteristics of the routes themselves and socio-demographic characteristics of individuals are found to influence route choice decisions.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 it