Mechanism of Early-Onset Breakdown at On-Ramp Bottlenecks on Shanghai, China, Expressways
Bibliographic record
Abstract
The mechanism of early-onset breakdowns was studied at on-ramp bottlenecks on expressways in Shanghai, China. From four on-ramp breakdown events captured on video, key parameters were extracted: prequeue flow, queue-discharge flow, speed variation per minute, lane change (LC) times in the mainline lanes and the acceleration lane, LC types, and LC locations (longitudinal and lateral). A total of 1,583 LC samples were analyzed. The findings showed a great difference in LC patterns when breakdowns occurred earlier than normal (i.e., before the bottleneck reaches expected capacity). In the case of an early breakdown, most LCs were forced LCs that occurred near the downstream end of the bottleneck, which spread laterally rather quickly. In contrast, in normal breakdowns in the United States, LCs were mostly free LCs that occurred evenly along the bottleneck longitudinally but were concentrated in rightmost lanes laterally.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".