Incremental Modeling Developments in Sacramento, California: Toward Advanced Integrated Land Use-Transport Model
Bibliographic record
Abstract
The regional transportation planning agency in Sacramento, California, is taking a three-pronged approach to updating its land use-transportation forecasting models: developing a long-term model design, improving existing models toward that design, and collecting data that can be used to support the existing models while the new design is being developed. The advanced integrated model design contains a tour-based travel model involving microsimulation of individual tours of synthetic households, a microsimulation-based land development model, and a spatial input-output model of the regional economy. The existing models consist of a land use model based on the MEPLAN model, a traditional four-step model of transportation demand improved with the addition of an automobile ownership submodel and joint consideration of mode and destination for work trips, and an interactive neighborhood-level parcel allocation system. The process described is one of improvement of current models and of moving toward a new model design while the agency faces ongoing modeling needs and uncertain budgets.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".