Optimization of coordinated signal settings for hook‐turn intersections
Bibliographic record
Abstract
Summary Hook turn (HT) is a unique traffic regulation rule for right‐turning vehicles at intersections (in the system where driving is on the left), which was proposed in Melbourne to improve the safety level and operational efficiency of intersections. However, existing coordination plans for HT intersections are fixed and determined empirically, which restricts the further improvements of the efficiency. In this paper, mathematical models are developed for the calculation of the average vehicle delay, with consideration of the spillback phenomenon of HT vehicles in waiting areas. The platoon dispersion model is used to describe the traffic movements between coordinated intersections. With the aim of minimizing average delay of all vehicles, a mixed nonlinear integer model is developed for the optimal coordination plan, which is solved by a genetic algorithm due to the complexity of the model. Finally, a numerical example is built based on three HT intersections in downtown Melbourne, to verify the proposed methodology. Based on a comparison with the current signal plan, the optimal signal plan can significantly reduce the average delay as well as the number of spillbacks, in both the peak hour and off‐peak hour cases. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".