Framework for Reliability-Based, Safety-Explicit Horizontal Curve Design using Naturalistic Driving Data
Bibliographic record
Abstract
The design of horizontal curves is generally based on a deterministic analysis of driver comfort using a point-mass model based on data mostly from the early 1900s and might be outdated. Furthermore, the design lacks quantitative safety evaluation. This paper proposes a new framework for horizontal curve design that provides a quantitative evaluation of safety performance. The framework uses recently collected data from the naturalistic driving study (NDS) to develop models to predict distributions of parameters that reflect contemporary driver behavior, such as speed on curve and comfort threshold in favorable weather and rainy conditions. Statistical models were also developed for more accurate evaluation of vehicle dynamic parameters than the point-mass model. The variability of driver behavior and vehicle dynamics parameters was considered using reliability analysis to develop surrogate safety measures for four design criteria: vehicle stability, comfort threshold, sight distance, and rollover. Safety performance functions were then developed to relate reliability indices to expected safety performance. The results showed that only the driver comfort criterion was not significantly related to expected safety performance. A design example was presented using the proposed framework, which showed the expected change of safety performance of the curve being considered. An optimum radius was then found to minimize the number of expected collisions.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".