Modeling Speed and Comfort Threshold on Horizontal Curves of Rural Two-Lane Highways Using Naturalistic Driving Data
Bibliographic record
Abstract
Modeling efforts for driver behavior parameters on horizontal curves have mostly focused only on the 85th percentile value. However, predicting whole distributions would help improve alignment design by allowing reliability-based design and design consistency evaluation. This paper used naturalistic driving study data to model distributions of speed and comfort threshold on horizontal curves of two-lane rural highways. Several variables along the approach tangent and curve were extracted and examined. This analysis helped determine the driver behavior parameters needed to evaluate driver behavior on horizontal curves and the headway threshold for free-flow conditions. Driver level models (DLM) and panel models (PM) were developed to predict distributions of curve speed and comfort threshold in addition to the traditional 85th percentile models. The models developed can be used in evaluating vehicle stability, driver comfort, and design consistency. Thus, the models can act as the basis for reliability analysis of horizontal curves, for which analysis methods are already established but realistic data are relatively scarce.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".