Modeling Driver Speed Behavior on Horizontal Curves of Different Road Classifications
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
Considerable evidences have been presented by many researchers on the safety benefits of using the expected operating speed on highway alignments as a basis for alignment design. Subsequently, the design practices of many countries have adopted an operating speed approach as a replacement to the traditional design speed approach. Accordingly, considerable research efforts have been drawn worldwide to operating speed prediction over the past decades. However, most of the previous research concentrated on two-lane rural highways only, and there has been little work conducted for other road types. Some prediction models are questionable due to the bias or human errors induced by manual speed measurements. This research proposed a field experiment to analyze driver speed behavior on the most common road types in Eastern Ontario, including freeway interchanges. Speed prediction models were developed, using actual driving data, for two-lane rural highways and urban/suburban roads. The models consider driver speed behavior when negotiating horizontal curves.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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