Expressway Single-lane Work Zone Capacities with Commercial Vehicle Impacts
Bibliographic record
Abstract
The Ontario Ministry of Transportation operates and maintains an extensive system of freeways and lesser highways across the province. Due to climatic considerations, rehabilitation and improvement activities are concentrated during the non-winter months, meaning that at any given time, there may be a number of work zones active on the freeway system in the Greater Toronto Area. It was believed that the relatively simplistic planning assumptions concerning the capacity of single-lane work zones may not reflect actual operations and it has been observed that overnight construction ?windows? defined on the basis of these assumptions are often subject to significant queuing and delays. In an attempt to understand work zone capacity, data was collected at seven work zones in the Toronto area during the fall of 2008. Analysis of this data indicated a wide and unexplained variation in the capacities achieved at the surveyed locations. It was hypothesized that lane-changing beharviour may be an important factor in the explanation of this variation. Using micro-simulation, the effects of different lane-changing assumptions were evaluated and a pattern of operations corresponding to the observed data was achieved. It was observed that there are two operational regimes, one with higher throughputs corresponding to early lane changing and relatively smooth operation in the remaining open lane. The second, with lower throughputs, involved interrupted flow in the open lane. These results are preliminary and would benefit from data collection and analysis oriented to the evaluation of this particular phenomenon.
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 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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".