Study on the Ways to Improve Deep Underground Road Facilities and Operation Based on the Cases of Longitudinal Tunnel
Bibliographic record
Abstract
Recently, starting with the deep underground road construction plan in Seobu Expressway, Korea, there area many studies on deep underground roads to be newly built. However, there is an extreme lack of safety standards, which does not consider traffic conditions and road driving characteristics. Therefore, this study reviewed safety elements to reflect in the deep underground road planning by analyzing driving stability of longitudinal tunnels with road environments, which resemble deep underground roads. For comprehensive analysis, the characteristics and causes of the accidents that have occurred in seven longitudinal tunnels with a length of 2km or over in Gangwon area, were collected. Specifically, geometric structures and facilities of each tunnel were investigated. Also, the present state of facility installation and the changes in driving speed of vehicles passing through each tunnel were observed to analyze the causes for the traffic accidents in each tunnel and accident reduction alternatives. It was revealed that the most frequent accidents in the tunnels resulted from the changes of traffic flow due to the abrupt speed reduction of forward vehicles, or the failure in speed control of following vehicles during the traffic congestion situation. Moreover, installing facilities such as plane and longitudinal curves, median strips and marginal strips seem to induce consistent driving speed. These results mean that for accident prevention, speed management must be preceded and there is a need to develop and introduce safety facilities actively to control the driving flow of forward and following vehicles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".