Unifying Long and Short Distance Personal Travel in a Statewide Planning Model
Bibliographic record
Abstract
A disaggregate behavioral tour-based microsimulation model was developed to forecast intrastate long-distance personal travel on a typical weekday as part of an overall statewide travel model system for all residents of California. A novel approach to what is traditionally described as travel generation was developed as a series of choice models focusing on consistency and integration with other components of the model system. Key features include the explicit integration of this long-distance personal travel model with the complementary short-distance personal travel model, the specification of a travel party size model for long-distance travel based on household size, and the development of models to represent characteristics of the long-distance travel tour, including duration of tour (number of nights), travel day status for a typical weekday, and time of travel within the weekday. The explicit trade-off between long- and short-distance travel produces appropriate sensitivities and reproduces the real...
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".