Safety Comparison of Simple and Spiral Horizontal Curves Based on Side Friction Factor Dynamic Modeling
Bibliographic record
Abstract
A horizontal curve’s geometric design is considered an important factor in highway accidents, and simple and spiral curves are regarded as the most common types of horizontal curves. Various factors affect the safety of horizontal curves, one of the most important of which is the side friction factor in the horizontal curves. Therefore, in this study, the safety of simple and spiral horizontal curves was investigated for the E-class sedan, E-class SUV, and two-axle conventional truck based on the side friction factor. In this regard, CarSim and TruckSim vehicle dynamic simulation software were utilized using 360 scenarios, including vehicle speed, vehicle type, curve radius, and road geometry. It was revealed that the maximum side friction factor for all vehicles in the simple horizontal curve was higher than the spiral horizontal curve. Also, the process of increasing the side friction factor was carried out with a gentler slope in the spiral horizontal curve. Except for the radius of 0.7 times the maximum radius of the spiral horizontal curve (R) for the truck and the radii of 0.7 R and 0.9 R for the sedan and SUV, the maximum side friction factor in simple and spiral horizontal curves was lower than the AASHTO recommended values, which shows that the spiral horizontal curve was better and safer compared to the simple horizontal curve based on the side friction factor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".