Empirical Evidence on the Existence of Collateral Anomaly
Bibliographic record
Abstract
Two-dimensional microscopic traffic models have been introduced in recent years, opening new lines of research. If properly developed, they may allow more accurate reproduction of traffic behavior, enhancing the effectiveness and application field of traffic simulation. This work analyses a particular behavior of drivers regarding the second dimension of driving (the lateral direction) that the authors call collateral anomaly. It encompasses the peculiar decisions and actions taken by them when two vehicles are right next to each other. In order to achieve that, data extracted from trajectories observed in U. S. highways is used. After the analysis, the authors discuss how these results, as well as others previously reported by different authors, are consistent with the idea of collision avoidance maneuvers executed by drivers due to sudden changes in the trajectories of neighboring vehicles. To verify this behavior on drivers is critical to characterize the way in which vehicles move in the lateral direction. The inclusion of such a behavior into a traffic model might improve the accuracy of traffic modeling, and would help to understand other behaviors observed in the field, which might be related with the lateral movement of vehicles, such as the relaxation phenomenon (Laval and Leclercq, 2008), or the influence of the lane’s and vehicle’s width in traffic (Bartel et al., 1997).
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".