Three-dimensional GIS-based approach for highway design consistency evaluation
Bibliographic record
Abstract
The mission of transportation is to transport people and goods safely and efficiently. Therefore, traffic safety has been one of the most important topics since the birth of the subject of transportation. Improving highway design consistency is considered as an important strategy for improving traffic safety. Geographic information systems (GIS) has been popular for decades due to its great ability to deal with spatial or spatially-related data. Contributions from GIS to transportation have become well known in some aspects. However, GIS, especially its 3D visualization function, has not, in previous studies, been integrated into the core of the highway design consistency evaluation procedure. In contrast, the major objective of this thesis research is to integrate the latest advanced GIS techniques including its 3D visualization function and the state-of-the-art knowledge from previous studies into the highway design consistency evaluation procedure. By adding new functions specifically developed for highway design consistency evaluation, a 3D GIS-based highway design consistency evaluation methodology is developed. This newly developed methodology and associated software tools, as a combination of GIS, including its 3D visualization function, and highway consistency modules, will make significant contributions in the following aspects: highly automated consistency evaluation procedure, 3D-alignment-based consistency level analysis, impressive evaluation result presentation, and spatially based consistency improvement suggestion. Verification of this methodology on a typical 3D-highway segment in Ontario shows very promising results. This study, to a great extent, is convincing that, in the near future, designers could be able to design highways in a regular GIS environment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".