Minimum Lengths of Acceleration Lanes Based on Actual Driver Behavior and Vehicle Capabilities
Bibliographic record
Abstract
The current geometric design guide in the US uses design values for the required lengths of acceleration lanes that are based on research studies conducted more than 50 years ago. Those design values need to be updated to reflect current drivers’ behavior patterns and vehicle mechanical characteristics. This paper presents a new method for determining the required lengths of acceleration lanes at freeway interchanges based on actual driver behavior and vehicle acceleration capabilities. Realistic acceleration profiles were established for different drivers accelerating from the design speed of the entrance ramp to that of the highway into which they are merging. The acceleration profiles were established based on field data collected using Global Positioning System (GPS) data-logging devices that recorded the positions and the instantaneous speeds of different vehicle types piloted by different drivers at 1-s intervals. Design tables were developed for different grades to help designers select the required length of the acceleration lane based on the design speeds of the freeway and the entrance ramp. The developed design tables have the potential to provide design values for the lengths of acceleration lanes that are more realistic and representative of current vehicle and driver characteristics.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.007 | 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".