Modeling Speed and Comfort Threshold on Horizontal Curves of Rural Two-Lane Highways Using Naturalistic Driving Data
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it