An Empirical Investigation of Speed Patterns on S‐Curves Using Naturalistic Driving Data and Mixed Logit Model
Bibliographic record
Abstract
S‐curves are common and complex curves in road networks, consisting of two curves in opposite directions connected by a short segment. Drivers’ behavior on S‐curves reflects their ability to navigate complex environments, which is not yet fully understood. This study examines speed patterns on five S‐curves using a naturalistic driving dataset of 2676 vehicle trajectories. A hierarchical clustering method is proposed to identify typical speed patterns on S‐curves, and a mixed logit model is developed to analyze the influence of curve radius, slope, and lighting conditions on drivers’ speed pattern choices. The empirical findings reveal that (1) each S‐curve has dominant speed patterns, and these patterns vary across different S‐curves; (2) speed patterns generally exhibit three types of shapes: S‐shaped, U‐shaped, and anti‐S‐shaped; and (3) the curve radius, the difference in radii between the two adjacent curves, the slope, and lighting conditions significantly influence drivers’ speed pattern choices. These results provide valuable insights for S‐curve alignment design, speed guidance strategies, and speed planning algorithms for autonomous vehicles.
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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 | 0.000 | 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 | 0.001 |
| 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 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".