Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment
Bibliographic record
Abstract
Abstract The connected vehicle (CV) technology can benefit signal coordination with fine‐grained spatial and temporal vehicle and infrastructure data via real‐time communication. Although CV‐based signal coordination systems have been investigated from offline and online strategic perspectives, existing works have yet to address certain coordination performance issues, including the dynamic platoon dispersion effect and low penetration impact. Targeting at resolving these issues, this work proposes a real‐time predictive coordination method consisting of a probabilistic single‐vehicle‐based dynamic platoon dispersion model, an extended link performance function, and a real‐time model predictive control (MPC)‐based coordination framework. The proposed coordination method was comprehensively investigated by a software‐in‐loop simulation platform with different practical corridor scenarios in the ACTIVE CV testbed in Canada. Results show the proposed coordination control continuously outperformed existing signal control with lower delays for major streets with different demand profiles and different CV penetration rates, even in low penetration conditions. In conclusion, the proposed CV MPC‐based coordination can offer significant potential to further improve the system performance of signal coordination in a low penetration environment; therefore, it has the potential to enhance other CV‐based signal control applications in the initial deployment stage of CV technology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".