Investigating the Safety of Run‐Off‐the‐Road Vehicles on Vertical and Horizontal Curves With the Foreslope Using Multiple Regression Analysis
Bibliographic record
Abstract
The run‐off‐the‐road (ROR) vehicle from the curves, as one of the most accident‐prone sections of roads, has always received special attention. Centrifugal force on vehicles and human error are the two main causes of accidents in these areas, which will eventually lead to overturning or sliding of vehicles. Based on previous research, few studies have been conducted on the influence of friction factors over horizontal and vertical curves with foreslopes for ROR vehicles considering various factors such as vehicle type, speed, departure angle, and foreslope slope through the vehicle dynamics simulation. Thus, in this research, the safety of ROR vehicles on curves over the foreslope was investigated from the perspective of the vehicle dynamics simulation. Finally, by simulation outputs for each of the vehicles used (Sedan, SUV, and truck), a multiple regression modeling was presented to examine the side friction factor of horizontal and vertical curves with a foreslope. The results showed that for horizontal curves, the first third of the beginning of the curve was the most dangerous part when vehicles deviated from the curves. Also, in vertical curves, the departure angle of 15 and 25° for vehicles, and foreslopes of 1: 3 and 1: 4, had the greatest effect on the overturning points of the vehicles. In addition, trucks had fewer friction factors at all speeds in comparison with Sedans and SUVs, and consequently, they had lower skidding potential in all specified conditions. On the other hand, an increase in skidding potential was observed in all tests on steeper foreslopes, which was caused by increasing the side friction factors and decreasing the margin of safety of vehicles on these types of foreslopes. Finally, based on the multiple regression analysis, the best model was presented to predict the side friction factor for various vehicles on horizontal and vertical curves with a foreslope, and it was indicated that the obtained models had a good correlation for all the test conditions. The study’s findings can be applied to improve road safety by modifying road geometry, adjusting foreslope angles, enhancing pavement friction, and informing vehicle design and driver education programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".