Spatial Transferability of Tour-Based Time-of-Day Choice Models
Bibliographic record
Abstract
An empirical assessment of the transferability of tour-based time-of-day (TOD) choice models across different counties in the San Francisco Bay Area of California is presented. Transferability was assessed with two approaches: (a) an application-based approach that tests the transferability of a model as a whole and (b) an estimation-based approach that allows the analyst to test which specific parameters in the model are transferable. Also tested was the hypothesis that pooling data from multiple geographic contexts helps in developing models with better transferability than those estimated from a single context. The estimation-based approach yielded encouraging results in favor of transferability of the TOD choice model, with a majority of parameter estimates in a pooled model found to be transferable. Pooling data from multiple geographic contexts appears to help in developing better transferable models with better transferability. However, attention is needed in selecting the geographic contexts from which to pool data. The pooled data should exhibit the same demographic characteristics and travel level-of-service conditions as in the application context.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".