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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".