An enhanced pricing model for truck transportation: a case study in Swedish forestry
Bibliographic record
Abstract
Abstract Determining freight rates for heavy trucks involves a detailed analysis of multiple cost factors, including time, distance, fuel, and other operational costs, which collectively contribute to the overall compensation for transportation services. However, actual remuneration is based on more simplified agreements. Often, the standard agreement is based on the loaded driving distance. Such agreements provide an accurate description of the average cost over many transports but can be very unfair in compensation on single transports. This paper presents a pricing model for truck transportation that extends traditional models based on distance. The new model includes a measure of cost driving factors along the route, such as hills, road surface, curves, speed limits, intersections, speed changes, long ascents, and other physical difficulties. This measure is extracted from the Calibrated Route Finder, a route selection support system used for roundwood transportation in Sweden. The suggested price model that combines distance and a weighted resistance measure gives a better match between remuneration and full costing of a transport than a model that concentrates only on distance. The suggested model has been tested on a large annual transport data set and detailed and selected transportations evaluated by five large forest companies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".