Performance-Based Geometric Design Analysis System for Capital Project Development
Bibliographic record
Abstract
Various performance-based geometric design tools have been developed and used over the past ten years. Most of the tools consider only one aspect, such as safety or sustainability. For highway rehabilitation and new construction projects, an integrated design tool is needed. This paper demonstrates a Network Expansion Support System (NESS) developed by Alberta Transportation in Canada. NESS integrates the geometric design guide, safety and level of service criteria, as well as budget and risk management. It screens highway segments for deficiencies and provides recommendations. With this system, the selected design alternatives will not only meet the geometric design standards but also achieve better network performance. Furthermore, NESS belongs to the provincial Transportation Infrastructure Management System (TIMS), so the recommended highway segment work plans are rationalized with other activities, leading to a more efficient capital work program. This paper presents case studies on project development for highway improvement using NESS. It also demonstrates how TIMS manages the performance of highway networks. Potentially, highway agencies could adopt NESS’ approach in the project development stage to identify geometric design and performance deficiencies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".