Optroad: A Computer Program For Interurban Road Network Planning
Bibliographic record
Abstract
In this paper, we present OptRoad, a user-friendly, optimization-based computer program aimed at helping transportation authorities in the long-term interurban road network planning. The core of OptRoad is an optimization model with a multi-level (discrete) nature, in which roads are defined according to some hierarchy (e.g., freeways, fast highways, and slow highways). Road investments are decided taking into account the planning framework typically used in practice, which is based on the concept of level of service. In addition, the optimization model can enclose more than one objective – equity, robustness, and energy objectives can be added to the traditional efficiency objective. The applicability of OptRoad is illustrated through an academic example based on a main road network of the state of Parana, Brazil. This case study was included to clarify the type of results that can be expected when the proposed approach is used. As illustrated by the Brazilian case study, we believe that OptRoad can be a helpful computational tool to help decision makers in the long-term planning of interurban road networks. Although it is not yet in a final version, OptRoad has already a stable version and, with few improvements, can easily be used by third party users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".