Sight-Distance Requirements for Left-Turning Vehicles at Two-Way Stop-Controlled Intersections
Bibliographic record
Abstract
The current highway geometric design guide provides a method for calculating intersection sight distance at two-way stop-controlled intersections by assuming that a departing driver on the minor road that would start from a resting position needs a fixed-time gap for departing the intersection regardless of the design speed or the grade on the major road. However, departing drivers may fail to perceive the speeds of the approaching vehicles on the major road in order to judge the available departure gap and decide whether or not to accept it. In this paper, a novel method is introduced to determine intersection sight distance requirements for two-way stop-controlled intersections based on actual drivers’ behavior and vehicle capabilities. The method incorporates acceleration profiles for vehicles starting from rest, which were developed based on field data collected using global positioning system (GPS) data loggers that recorded the positions (latitudes, longitudes, and altitudes) and the instantaneous speeds of different vehicle types piloted by different drivers at 1 s intervals. Design tables and an application example are presented to help designers select the required intersection sight distance based on the design speed and the grade on the major road.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".