Bibliographic record
Abstract
Passing an impeding vehicle on a two-way two-lane roadway is a complex maneuver because of the variety of passing conditions and driver behavior. In this study, the supposition that passing maneuvers can be classified on the basis of a quantitative description of passing behavior was examined by analyzing data collected during a passing experiment conducted in a driving simulator. Evidence was found to support the following hypotheses: ( a) the speed increase of the passing vehicle during the passing maneuver is smaller when the speed difference between the passing and impeding vehicles at the moment of initial acceleration is greater and ( b) the speed reduction of the passing vehicle during the latter portion of the passing maneuver is greater when the time to collision with the oncoming vehicle at the moment when the passing vehicle returns to the right lane is greater. Therefore, it was concluded that the start of a pass can be classified by acceleration behavior, and the end of the pass can be classified by deceleration behavior. This behavioral approach is an improvement to classifying passing maneuvers on the basis of a qualitative assessment of the passing conditions, as in establishing the AASHTO passing sight distance design criteria and the minimum passing sight distances in the Manual on Uniform Traffic Control Devices for Streets and Highways. A particular passing behavior, described by a specific acceleration and deceleration behavior, could be used to modify or update these criteria, thereby improving the guidance given to passing drivers and potentially the safety of passing areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".