A new method for determination of time-of-day breakpoints based on clustering and image segmentation
Bibliographic record
Abstract
Signal control is an important part of the transportation system and plays an important role in improving the capacity of intersections. This paper proposes a new traffic time division method for multiperiod fixed-time control strategy. Firstly, we put forward a new concept — transportation overlap rate — to complete the clustering of daily traffic flow patterns. Then, all the daily traffic flow data belonging to the same category are composed into a matrix, which is converted into the corresponding image later with the aim of using the fast and robust fuzzy C-means clustering (FRFCM) method to segment it. Finally, the traffic time division and breakpoint location are obtained through further analysis and processing of the segmentation results. For each period, the optimal signal cycle and green split are separately calculated by Webster’s signal timing method to satisfy different traffic demands of each period and effectively improve the operation efficiency of the intersection. The simulation results at a certain intersection in the city of Mianyang demonstrate the effectiveness and practicability of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".