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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".