Driver Behavior on Exit Freeway Ramp Terminals Based on the Naturalistic Driving Study
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
Using trip data from the SHRP-2 Naturalistic Driving Study (NDS) database collected at 12 sites in three states across the United States, this paper investigates driver behavior at freeway exit ramp terminals. First, the study qualitatively assesses driver speed behavior as they navigate the speed change lane (SCL) and the ramp. Starting at the beginning of the SCL and continuing after diverging onto the ramp controlling curve, a trend of continuous vehicle deceleration was evident, which continued throughout the SCL and ramp. It was also evident that a portion of drivers have a tendency to diverge onto the SCL on the taper and before the SCL has begun, where this behavior is dominant on the taper-type SCL. In general, statistical analysis revealed that the speed measures of driver behavior follow a normal distribution. The speed and deceleration measures at the study sites were statistically and significantly different, with the differences likely related to the geometric characteristics of each site. The data were then used to develop prediction models for the speed and deceleration measures. To account for the repeated measures induced by the same drivers in the dataset, linear-mixed models were developed for the speed and deceleration behavior measures.
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.
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 | 0.001 | 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