Los Angeles Cargo Forecasting Model Development
Bibliographic record
Abstract
The Los Angeles County Metropolitan Transportation Authority (Metro) has developed a freight forecasting model based on a hybrid approach, which addresses many of the short-comings in current freight forecasting procedures. The hybrid approach combines commodity flow models to estimate the interregional or long-haul freight flows with truck models used to estimate the local truck or short-haul freight flows. This approach is combined with new techniques to model trip chains for goods moving through warehouse and distribution centers and for service vehicles making multiple stops on a daily tour. The development of this model has been successfully calibrated and applied for the Southern California region (six counties) and estimates freight flows between Southern California and the rest of the U.S., as well as Mexico and Canada. The focus of this paper is on model development rather than calibration and application. The model calibration and validation results, along with an initial forecast for the year 2030 are the subject of another paper on this model for the TRB Planning Applications Conference in May 2007.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".