Estimating Traffic Changes and Pavement Impacts from Freight Truck Diversion following Changes in Interstate Truck Weight Limits
Bibliographic record
Abstract
This paper reports on two methodologies that were developed and used in a study for the State of Maine. The study examined the pavement, crash, and bridge costs of higher truck weight limits being allowed on an Interstate route. These higher weight limits would attract to the Interstate route high-weight (between 80,000 and 100,000 lb gross vehicle weight) combination trucks that currently use alternative routes on Maine state roads (which already allow these higher weight limits). The first methodology estimated the changes in freight truck traffic volumes. The methodology estimates gains and losses in vehicle miles traveled by route and by vehicle configuration and the associated gains and losses in equivalent single-axle loads (ESALs) on these routes. The second methodology estimated road cost per ESAL by road type; this allows pavement costs to be derived from the ESAL effects estimated by the first methodology. The data used for the methodologies included TRANSEARCH data, weigh-in-motion station data, traffic classification count data, and the Maine Department of Transportation's TIDE road database system. The traffic estimation methodology used successive (iterative) rounds of expert opinion derived through interviews, data analysis, and route mapping. This paper also discusses the key role of an evolving picture of the system within the analysis team.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".