Route selection for best distances in road databases based on drivers’ and customers’ preferences
Bibliographic record
Abstract
The importance of road databases for distance calculations and route selection is increasing. One reason is that payments and invoicing are often based on the distance driven. However, it can be hard to agree on a “best” distance because of drivers’ preferences. These preferences can be described by road features such as road length, quality, width, speed limits, etc. Moreover, a pure standard “shortest path”, which is often used in road databases, can result in a route that is considerably shorter than a preferred and agreed distance. Consequently, there is a need to find suitable weights for the features of the roads that provide fair and agreed distances at the same time for all users. We propose an approach to find values of such weights for the features. The optimization model to find weights is an inverse shortest path problem formulated in a mixed integer programming model. The approach is tested for the Swedish Forestry National Road database. Since 2010, it has been in daily use to establish distances and is available for all forestry companies and haulers in Sweden through an online system.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".