Evaluation of Driver Visual Demand on Three-Dimensional Rural Highway Alignments
Bibliographic record
Abstract
This research focuses on evaluating driver visual demand on three-dimensional (3D) highway alignments consisting of combined horizontal and vertical alignments which is an important part of highway design consistency research. Using a driving simulator, ten hypothetical 2D and 3D alignments for two-lane rural highways were developed, following the standard guidelines of the Transportation Association of Canada (TAC) and the American Association of State Highway Transportation Officials (AASHTO). Fifteen driver subjects drove in the simulator. The data relating to visual demand information were processed and analysed using Microsoft Excel and SAS statistical software. The results indicated that visual demand on 3D curves varies widely with the inverse of radius of horizontal curvature and the inverse of K value of vertical curvature. Age played another important role on visual demand. Models for evaluating visual demand on 3D highway alignments were developed for curves and tangents. The models developed in this study have been applied to horizontal and 3D alignments to carry out a design consistency evaluation. In addition, GIS virtual reality technique was applied to present the visual demand results for a real highway on the 3D visualization model. 3D visualization not only offers a better understanding of driver workload along the highway, but also represents an important tool to effectively manage information.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".