Integrating Equity Objectives in a Road Network Design Model
Bibliographic record
Abstract
The traditional approach to the road network design problem focuses on the optimization of network efficiency under a given budget. Generally, this leads to the improvement of roads next to the largest population centers, where travel demand is higher. Such results are not consistent with sustainable development principles, since the dissimilarities between the welfare of large and small centers will tend to increase. Nevertheless, equity issues were rarely taken into account in road network design. Moreover, all existing studies rely on a single equity measure. In this paper equity concerns in transportation planning are reviewed briefly, and a comparison of alternative equity measures is presented. Three equity measures were selected and incorporated into an accessibility-maximization road network design model. The three equity measures reflect different perspectives on equity: accessibility to low-accessibility centers, the dispersion of accessibility values across all centers (Gini coefficient), and the dispersion of accessibility values across all centers and across centers in the same region (Theil index). The implications of adopting each of these equity measures are illustrated through application of the optimization model to three random networks.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".