Platoon Intensity of Connected Automated Vehicles: Definition, Formulas, Examples, and Applications
Bibliographic record
Abstract
The strength of connected automated vehicles (CAVs) clustering, namely, platoon intensity, has an essential impact on the traffic capacity, safety, and stability of the mixed traffic flow with CAVs and human-driven vehicles (HDVs). This article proposes the definition, formulas, examples, analysis, and applications of CAV platoon intensity. Firstly, the CAV platoon intensity considering the maximum platoon size is defined, and its mathematical calculation method is developed. Secondly, the three types of value ranges for platoon intensity are discussed. Thirdly, the distribution characteristics of five types of time headways were proposed. Finally, the influence of CAV platoon intensity on the fundamental diagram and traffic safety of mixed traffic flow are analyzed from theoretical derivation and simulation evaluation, respectively. The result suggests the following: (1) According to the sensitivity analysis, an optimal platoon size of 5 vehicles was suggested for the CAV platoon; (2) The penetration rates and platoon intensity of CAVs can improve the capacity of mixed traffic flow, and the increase is gradually more significant; (3) The effect of the increase in platoon intensity on the capacity is not significant enough when the CAV penetration rate is low; (4) A higher platoon intensity is good for dissipating disturbances in the mixed traffic flow; and (5) CAV platoon intensity has a marginal impact on the characteristics of mixed traffic flow with the low penetration rate. It is noteworthy that a reasonable increase in platoon intensity can improve the traffic capacity and safety of mixed traffic flow.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".