Platoon-Based Assessment of Two-Way Two-Lane Roads Performance Measure: A Classification Method
Bibliographic record
Abstract
Two-way two-lane roads have a significant impact on road transportation infrastructure. Platoon formation on two-lane roads is one of the factors that affect the quality of traffic flow on two-lane roads. More specifically, the creation of a platoon increases the density of vehicles and the number of overtaking maneuvers and decreases the traffic performance of two-lane roads. The present study made an effort to investigate the effect of the platoon characteristics on the traffic flow of vehicles on two-lane roads. Moreover, it strived to develop the nonlinear regression model with a new approach to capacity calculation. Finally, a method for estimating the level of service (LOS) based on the number of followers per capacity (NFPC) and LOS classification using the KNN method was presented. Considering these aims, first, the relevant variables (which were related to platoon) including time headway (Ht), average travel speed (ATS), platoon size (PS), average platoon speed (APS), percentage of heavy vehicles (HV), percent time spent following (PTSF), number of overtaking (NO), density (ρ), and traffic flow were investigated on the examined roads. The results showed that the speed and the Ht were the most effective and the least effective platoon characteristics, respectively. Moreover, it was accompanied by the increase in PTSF which resulted in the increase in overtaking maneuver. Finally, the results regarding the developed model showed that the NFPC measure was able to predict the traffic flow of two-lane roads in a more satisfactory way compared to the two criteria, namely, PTSF and ATS in the Highway Capacity Manual (2016) since it increased ATS by 65% and decreased the capacity of two-lane roads by 21%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".