Macroscopic modeling of lane‐changing for two‐lane traffic flow
Bibliographic record
Abstract
Abstract We propose a macroscopic model of lane‐changing that is consistent with car‐following behavior on a two‐lane highway. Using linear stability theory, we find that lane‐changing affects the stable region and the propagation speeds of the first‐order and second‐order waves. In analyzing a small disturbance, our model effectively reproduces certain non‐equilibrium traffic‐flow phenomena—small disturbance instability, stop‐and‐go waves, and local clusters that are affected by lane‐changing. The model also gives the flow‐density relationships in terms of the actual flow rate, the lane‐changing rate, and the difference between the potential flow rate (the flow rate that would have occurred without lane‐changing) and the actual flow rate. The relationships between the actual flow rate and traffic density and between the lane‐changing rate and traffic density follow a reverse‐lambda shape, which is largely consistent with observed traffic phenomena.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".