Effect of driver and road characteristics on required preview sight distance
Bibliographic record
Abstract
Highway geometric design is a complex process that is closely related to human perception and behaviour. Among the human perception issues that can affect highway geometric design is the preview sight distance, which has been defined as the distance required to perceive a horizontal curve and react properly to it. Previous attempts to quantify preview sight distance included measurement on actual roads, physical modelling, and computer animation. This paper presents a computer animation experiment that was designed to examine the effects of geometric parameters and driver characteristics on preview sight distance and to statistically model preview sight distance. Statistical analysis showed that preview sight distance depends on geometric parameters such as the horizontal curve radius, use of spiral curve and its length, presence of crest vertical curve, algebraic difference of vertical grades, vertical curvature, and road delineation. On the other hand, driver characteristics were mostly found to be insignificant parameters. Finally, statistical models were developed to predict the value of preview sight distance using linear regression analysis. The models vary in simplicity and accuracy and were formulated as a function of the general alignment configuration or as a function of the exact geometric parameters.Key words: highway geometric design, sight distance, driver characteristics, three-dimensional alignment.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".