Work-zone traffic operation and capacity
Bibliographic record
Abstract
In assessments of the impact of expressway lane closures on traffic flow, current practice generally involves an assumption of the nominal capacity per lane which varies, in some cases significantly, by jurisdiction. Situations have been observed by the Ministry of Transportation of Ontario (MTO) where application of such a simplistic guideline, particularly in the absence of specific consideration of the effects of heavy vehicles in the traffic stream, has led to significant congestion issues and negative public reaction. Preliminary research, conducted on behalf of MTO with respect to overnight lane closures on MTO’s 400-series expressways in the GTA, suggests the existence of two operational regimes. The first regime, found where the demand is insufficient to result in queue formation at the lane closure and merging from the closed lanes is orderly, suggests a capacity in the order of 1,750 veh/h/lane, towards the higher end of the range of currently assumed values. The second regime, found where demand is such that a queue has formed, suggests a much lower capacity in the order of 1,100 to 1,300 veh/h/lane, towards the lower end of the range of currently assumed values. This research also looked at the equivalency of heavy vehicles in a work-zone, lane closure context. These results were based on relatively limited data and further data collection and analysis is recommended to verify these preliminary findings.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".