Estimation of speed differentials on rural highways using hierarchical linear regression models
Bibliographic record
Abstract
Large speed differentials between highway segments are associated with an increase in the number of accidents. Traditional speed differential measures, derived from single-level linear regression models, suffer from serious deficiencies, namely underestimating the speed differential (due to intracorrelated data) and inflating the adequacy of the model’s explanation (due to aggregate data). High-quality speed differential predictions are highly desirable right from the initial design phase, but the estimation process is not straightforward, and decision makers must recognize that speed differential predictions are subject to considerable uncertainty. This paper compares four models: two single-level models, a conventional multilevel model, and a Bayes multilevel model. The results show empirically that multilevel models increase the accuracy and precision of estimates of speed differentials, possibly with fewer data. The paper introduces a new, easy to interpret speed consistency measure that simply represents the probability that a vehicle exceeds a certain speed differential. This measure is calculated using a multilevel model and takes into account the uncertainty in the estimates of speed differentials. Overall, we show that a multilevel modeling approach can improve the quality of decision making that makes use of speed differential information in road design and road safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".