System Reliability Evaluation of Expressway Horizontal Alignment Design Considering Trucks and Passenger Cars
Bibliographic record
Abstract
The design of safe highway alignments is a complex task, with horizontal curves being the most critical components from both safety and operational perspectives. This is because, from the tangent to curve transitions, maintaining a constant operating speed in line with driver expectations rather than the designer’s judgment is essential from a safety standpoint. The relationship between the highway elements and static/dynamic characteristics of the various vehicles on the highway geometry motivates the development of reliable safety strategies for the curve design and safety evaluation. In this study, we selected passenger cars (PCs) and trucks as non-compliant drivers and passenger taxis (PTs) as compliant drivers to evaluate highway safety. We chose 22 horizontal curves from the Ghat region of India’s Mumbai to Pune Expressway. On selected curves, 5940 samples of vehicle spot speeds using a radar gun for PCs/trucks and data from 16 PTs using an e-tracker were collected. We then investigated a stopping sight distance and speed-based reliability framework for assessing horizontal curve design at the curve and alignment system levels. The results show that PCs in non-compliant conditions operate faster than PTs in compliant and trucks in non-compliant conditions. Therefore, the curve has a higher probability of non-compliance, P nc , (lower reliability) for PCs in non-compliant conditions, followed by PTs in compliant and trucks in non-compliant conditions. The higher P nc indicates more chances of crashes on the subject curves. Subsequently, we conducted a sensitivity analysis for the curve radius ranging from 400 to 900 m. The results show that the mid-ordinates must be changed to obtain a lower P nc by offering a longer sight distance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".