Parametric study of microscopic two-dimensional traffic flow models: A literature review
Bibliographic record
Abstract
Indian traffic is highly heterogeneous consisting of all-inclusive vehicle characteristics, occupying any lateral position over the entire road width which results in vehicles continuous interaction with the neighbouring vehicles (in both longitudinal and lateral directions), indicating two-dimensional (2D) traffic manoeuvre, opposite to the traditional one-dimensional (1D) interaction of vehicles in lane based traffic. Certain modifications were made in the existing 1D models to describe the overtaking and lane changing manoeuvre of the mixed traffic stream. However, the continuous lateral manoeuvre of the no-lane based mixed traffic cannot be described by these parameters. This paper initially provides a brief review of different 2D behavioural models, which describe the longitudinal and lateral movements simultaneously. Also, the various existing commercially available traffic micro-simulation frameworks developed for representing the real traffic are reviewed. Different microscopic traffic parameters used in the existing simulation models to mimic the real-world traffic are identified, which can be used to understand the 2D traffic stream.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".