Role of Freeway Ramp Geometry on Driver Acceleration and Merging Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it