Theoretical Research on Longitudinal Profile Design of Superhighways
Bibliographic record
Abstract
To improve driving safety on superhighways, longitudinal profile design parameters of a superhighway are calculated via force analysis while a car is driven on a slope. The calculations consider characteristics of drivers, cars, and roads. According to the vehicle type, design speed, and natural conditions, the maximum longitudinal slope of a superhighway is calculated and compared with those of an ordinary superhighway and high-speed railway. Based on analysis of the vehicle climbing performance, braking performance, and driver visual characteristics, the maximum and minimum slope lengths of a superhighway are calculated. By analyzing the elements of vertical curves, the minimum radius and minimum length of the vertical curves of a superhighway are calculated by considering factors such as mitigating the impact at the slope bottom, driving at night, and driving time along vertical curves. Analysis and calculation results show that when the maximum longitudinal slope is 2.50%, 2.25%, and 2.00%, the minimum slope length is 450 m, 400 m, and 350 m, respectively, and the minimum vertical curve length is 145 m, 130 m, and 115 m, respectively, and the superhighway travel requirements can be satisfied at speeds of 180 km/h, 160 km/h, and 140 km/h, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".