An investigation of merging and diverging cars on a multi-lane road using a cellular automation model
Bibliographic record
Abstract
In this paper,we have investigated two observed situations in a multi-lane road.The first one concerns a fast merging vehicle.The second situation is related to the case of a fast vehicle leaving the fastest lane back into the slowest lane and targeting a specific way out.We are interested in the relaxation time τ,i.e.,which is the time that the merging(diverging) vehicle spends before reaching the desired lane.Using analytical treatment and numerical simulations for the NaSch model,we have found two states,namely,the free state in which the merging(diverging) vehicle reaches the desired lane,and the trapped state in which τ diverges.We have established phase diagrams for several values of the braking probability.In the second situation,we have shown that diverging from the fast lane targeting a specific way out is not a simple task.Even if the diverging vehicle is in the free phase,two different states can be distinguished.One is the critical state,in which the diverging car can probably reach the desired way out.The other is the safe state,in which the diverging car can surely reach the desired way out.In order to be in the safe state,we have found that the driver of the diverging car must know the critical distance(below which the way out will be out of his reach) in each lane.Furthermore,this critical distance depends on the density of cars,and it follows an exponential law.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".