Offsetting Opposing Left-Turn Lanes for Intersections on Horizontal Curves
Bibliographic record
Abstract
Left-turn vehicles need sufficient sight distance to decide when it is safe to turn left crossing the lane(s) used by the opposing traffic. Current AASHTO policy recommends that the adequacy of sight distance for left turns should be checked for the reason that the opposing left-turn vehicles can block a driver’s view of oncoming traffic. Previous studies developed guidelines for offsetting opposing left-turn lanes to overcome this problem. However, these guidelines are only applicable to intersections with no curvature. This paper presents a mathematical model for calculating the required minimum left-turn lane offset and the median width (to accommodate the offset), when the intersections are located on horizontal curves. The provision of required offset ensures that the left-turn vehicles have unobstructed required sight distance. An application of the model is presented for divided highways with median width of 4.88 m assuming general values of other variables. The model is translated into an Excel worksheet in which the calculations for the required minimum offset and median width can be performed for any geometric configuration (e.g., curvature of major road, number of lanes, median and lane widths of the minor and major roads, etc.). Other design factors may be also input based on field observations, including design speed along the major road and longitudinal and lateral positioning of vehicles.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".