Role of Freeway Ramp Geometry on Driver Acceleration and Merging Behavior
Bibliographic record
Abstract
Design guidelines for freeway ramp entrances are based on speed and acceleration data collected before 1950. This study investigated driver behavior over the entire freeway entrance area, including the ramp, the acceleration speed-change lane (SCL), and the freeway right lane (FRL). Video-based trajectory and speed profile data were collected using unmanned aerial vehicles (UAVs) and were used for qualitative and quantitative analysis. General trends of the relationships between driver behavior measures and geometric characteristics of entrance ramp terminals were investigated under different traffic and design conditions. Results showed that vehicles tended to merge onto the freeway at relatively low speeds such that the difference between their mean speed at merging and that of FRL vehicles was statistically significant. Results also confirmed that SCL drivers tended to start acceleration after they passed the middle of the ramp controlling curve. Regression models were developed for predicting driver-vehicle behavior on SCLs and on-ramp curves using traditional regression for each parameter separately and simultaneous modeling using structural equation modeling. An example application is presented to demonstrate the use of the developed models in reliability analysis of entrance ramps, which can be used to establish probabilistic road design guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".