Speed development at freeway curves based on high frequency floating car data
Bibliographic record
Abstract
Road designers need to have insights where deceleration and acceleration are expected related to the position of the curve, and in in which amount so that drivers are able to safely decelerate and accelerate respectively into and out of a freeway curve. For this, empirical speed data is needed. Therefore, Floating Car Data in 153 curves in The Netherlands were collected at a resolution of 1 Hz and were filtered on free-flow periods, to analyse over 800 thousand unique continuous free-flow speed observations on these curves. Regression models were developed to predict speed development, including deceleration and acceleration behaviour upon entering and exiting freeway curves. The models rely on easy to generate geometric design variables, including the start and end position of the horizontal curve, the horizontal radius and the number of lanes. Using these variables, the designer can predict the speed development based on the 85th percentile of speed and acceleration, relative to the position of the curve. The regression models reveal strong goodness-of-fit of the predicted 85th percentiles of speed in a curve, showing acceleration and deceleration inside the curve, and higher predicted 85th percentile speeds than the design speeds. The models also show satisfying results in speed development prediction in sets of consecutive curves with different characteristics, as well as deceleration when entering a first curve and acceleration when exiting a last curve. These insights are valuable in evaluating road design in relation to traffic safety based on its predicted use.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".