Guidelines for Estimating Capacity at Freeway Reconstruction Zones
Bibliographic record
Abstract
This paper reports findings from recent investigations into freeway capacity at several reconstruction zones in Ontario, Canada. The aim is to provide guidelines for estimating freeway capacity at reconstruction sites. Findings are presented in two parts. The first involved results of individual investigations to estimate a base capacity at freeway reconstruction sites and the individual effect of several important factors that are believed to affect this capacity, namely; the effect of heavy vehicles, driver population, rain, site configuration, work activity at site, and light condition. In the second part, attempts to model work zone capacity are presented. Initially, two types of “site-specific” capacity models were developed using different analytical techniques at sites that have the most extensive and comprehensive capacity data. Finally, a “generic” capacity model for freeway reconstruction sites is proposed based on results from the individual investigations and the site-specific models. The proposed model suggests a base capacity value of 2,000 passenger cars per hour per lane for reconstruction sites under favorable conditions. Heavy vehicles and driver population were found to have the most significant effect on capacity.
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.
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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.011 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".