Development of platoon‐based actuated signal control systems to coordinated intersections: application in corridors in Houston
Bibliographic record
Abstract
A signal control method considering platoon‐based actuation is presented with a focus on its application to two arterial corridors in the Houston Metropolitan Area, where the signal coordination is required for adjacent intersections. For the purpose of immediate implementation, the system was developed based on existing conventional loop detectors and control technologies used by Texas Department of Transportation. Simply based on volume and occupancy – the only traffic information reported by a conventional loop detector, it is impossible to precisely detect a platoon of vehicle approaching an intersection. As a compromise way, two specific logics were proposed to switch the system between the original coordinated control and the new platoon‐based actuated control systems. At one testbed suffering from large left‐turn traffic, the results revealed by the detector data before and after the implementation show that the model works well to relieve the delay for both left‐turn traffic and through traffic on the major road. On another testbed where the platoon‐based control is activated only when the through traffic is not high, some improvements were also observed. This method opens the door of wide application of the platoon‐based signal control system (simply based on conventional loop detectors) to arterial corridors.
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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 | 0.000 |
| 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) | 0.001 | 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".