A mesoscopic whole link continuous vehicle bunch model for multiclass traffic flow simulation on motorway networks
Bibliographic record
Abstract
Modeling of heterogeneous driver behaviour is vital to understanding of dynamic traffic phenomenon taking place on motorway networks. In this research, we present a mesoscopic whole link continuous vehicle bunch model for multiclass traffic flow simulation on motorway networks. Two main attributes of traffic flow classification have been used are: (i) vehicle type, specifying in turn a vehicle length and, together with type of a preceding vehicle, time headway; and, (ii) desired speed, defining together with the speeds of the neighbouring vehicles, the vehicle acceleration/deceleration mode. It is assumed that vehicles in uncongested to moderate congested flow move in bunches dividing the drivers into the two main groups: (i) independent “free” drivers which usually manifest themselves as leaders of bunches; and, (ii) followers, or drivers which adapt their speed to the leader’s speed and follow each other at constrained headways specified by predecessor/successor pairs. The model proposes a solution to arbitrary traffic queries involving a motion in bunches having various speed and size by assuming the rate of driver arrivals follows semi-Poisson distribution and proportion of free drivers is predefined. The solution, assuming limited overtaking possibilities for all drivers, involves formation of longer queue behind bunches moving with slower speed and transformation of some of the “leaders” into “followers” because of adjustment their speed to the speed of the preceding slow-moving bunches. The present solution considers both stochastic and deterministic features of traffic flow and, therefore, may be easily extended to a specific uncertainty level.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".