Probability distribution‐based model for work zone capacity prediction
Bibliographic record
Abstract
Summary This study aims to develop a truncated lognormal distribution model to predict work zone capacity. The distribution model parameters are formulated as linear functions of road type, number of closed lanes, number of opened lanes, lane closure location, work duration, work intensity, work time, heavy‐vehicle percentage, and capacity measurement method. To reflect the uncertainty, a prediction band is constructed for the mean work zone capacity. The maximum likelihood estimation technique is used to determine the coefficients of the variables included in the probability distribution‐based capacity model. The sensitivity analysis results confirm that the proposed model has the capability of accurately predicting the mean and prediction band of work zone capacity at any given confidence level. In addition, it is found that work zones located in urban roads, with a large number of opened lanes, low heavy‐vehicle percentage, or long‐term work duration, have larger mean work zone capacity. A work zone with the closure of the right lane has bigger variability of work zone capacity than a work zone with the left‐lane closure. In addition, the measured work zone capacity is a little bigger if the hourly traffic volume converted from the maximum 3‐/5‐/15‐minute flow rate is considered as the work zone capacity. The proposed probability distribution‐based capacity model can help traffic engineers to evaluate the variability of work zone capacity and travel delay range. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 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".