Investigation of Early-Onset Breakdown Phenomenon at Urban Expressway Bottlenecks in Shanghai, China
Bibliographic record
Abstract
Based on the analyses of recurring bottlenecks in Shanghai’s expressways using spatial-temporal diagram, three typical isolated bottlenecks (a lane drop, an on-ramp, and a weaving section) were selected and method of transformed curves was adopted to investigate the traffic flow characteristic using the loop detector data. The starting and ending times of the three kinds of bottlenecks, pre-queue flow (PQF), queue discharge flow (QDF) were analyzed. Finally, some conclusions were discovered that QDF was higher than PQF in bottleneck sections of lane drop and on-ramp, the average difference were 18% and 27% respectively, but the situation was different in weaving section, the average rate of reduction was 22%. The findings were obviously different from results of diverse bottlenecks in other countries (e.g. M4 motorway near London, United Kingdom, I-494 in Minneapolis, Minnesota, USA, the Queen Elizabeth Way (QEW) and the Gardiner Expressway in metropolitan Toronto, Canada), i.e., the bottlenecks in lane drop section and on-ramp bottleneck have the early-onset characteristics. At last, the reasons of early-onset breakdown were preliminarily discussed from the aspects of driving behaviors, the temporal feature of flow rate and the feature of merging.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".