Revised Method for Calculating Departure Sight Distance at Two-Way Stop-Controlled (TWSC) Intersections
Bibliographic record
Abstract
This paper introduces realistic acceleration profiles for light-duty vehicles departing from rest at two-way stop-controlled (TWSC) intersections where minor roads (controlled by stop signs) intersect with uncontrolled major roads. The new profiles are based on current vehicle characteristics and driver behavior patterns. They are established based on actual field data collected using global positioning system data loggers that recorded the positional and speed data of various experimental vehicles starting from rest at TWSC intersections. Acceleration profiles are established in this paper and are used to develop a revised method for calculating the departure sight distance at TWSC intersections. Design tables were created to provide realistic sight distance values at TWSC intersections for different design speeds and number of lanes on the major road. It was found that the current values of intersection sight distance suggested by the design guides may be inadequate. Such values may force some approaching drivers on the major road to reduce their speeds or move to different traffic lanes to avoid conflicting with the departing vehicles. These maneuvers may have negative impacts on traffic safety. Therefore, implementing the revised method for calculating intersection sight distance, as presented in this paper, may ultimately reduce traffic collisions at TWSC intersections.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".