Understanding the Trigger of Breakdown at Tunnel Sag Section and Exploring the Cause with an Improved Car-Following Model
Bibliographic record
Abstract
Sag is a road section with significant vertical gradient change from downhill to uphill, which is a capacity bottleneck that easily causes traffic flow breakdown. To understand the trigger of breakdown, a typical sag section in Xiangyin Tunnel, Shanghai, is investigated using loop detector data in this paper. The empirical analysis indicates that the breakdown is mainly caused by the car-following behavior. Thus, an improved car-following model is proposed to explore the cause of breakdown by incorporating the gravity factor and several human factors (perception error and the asymmetry between acceleration and deceleration process). Combining a simple but effective lane-changing model, numerical simulations are conducted to analyze the influence of these car-following factors on traffic capacity. The results show that the proposed car-following model can describe the traffic flow characteristics of sag section, and the asymmetry is found as the critical internal cause of sag breakdown.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".