Research on Graded Lane Changing in Undersea Tunnel Exit Diversion Zones: Application of Set Pair Analysis and TOPSIS Method for Evaluation
Bibliographic record
Abstract
Owing to undersea‐tunnel constraints concentrating off‐ramp maneuvers in confined zones, this study optimizes graded lane‐changing strategies to mitigate collision risks. Using the Jiaozhou‐Bay Undersea Tunnel case, we propose an innovative exit diversion area graded lane‐changing strategy comprising Transition Section I, Transition Section II, gradient section, and auxiliary lane. Six schemes were simulated via UC‐WinRoad, with driver physiological stress quantified through Tobii eye‐tracking as a novel application of pupil dynamics. Four indicators—lane‐change position, lane‐change rate, pupil diameter, and speed change—were weighted by the integrated analytic hierarchy process and entropy weight method (AHP–EWM) methodology and evaluated via the set pair analysis with the technique for order preference by similarity to ideal solution (SPA‐TOPSIS) theory model. Optimal Scheme E (290‐m transition I, 210‐m transition II, 120‐m gradient, and 140‐m auxiliary lane) achieved γ = 0.968, significantly reducing pupil fluctuation by 32% compared with the shortest design (Scheme A) while ensuring smoothest speed control. This demonstrates effective conflict distribution in high‐risk undersea environments, providing universally applicable design benchmarks for tunnel safety enhancement.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".