Estimation of Time-Dependent Delay Models at Actuated Traffic Signals in Duhok City
Bibliographic record
Abstract
Duhok City, developed rapidly in recent years, leading to an increase in the traffic volume on its streets which are not sufficient for this increase resulting in traffic congestion. Therefore, this paper studied the delay as an important measure of effectiveness at traffic signals. The delay was measured and predicted (for saturated and under saturated conditions) to help in solving some underestimation problems by using the transportation system management (TSM) techniques. Five signalized intersections on Barzani major street in Duhok city was chosen. Vehicle delay was measured in these intersections directly from field, then it was calculated using the time-dependent equations recommended by three different official manuals, Highway Capacity Manual (HCM2000), Canadian Capacity Guide (ITE1995) and Australian Capacity Guide (ARRB1995). After that comparisons between the results of each theoretical method and the field result was made. Finally, the regression analysis technique was used to establish different relationships between the delay data obtained from the field and each of the theoretical methods to find the best applicable model to predict the vehicle delay at signalized intersections in Duhok City. It was proved that the logarithmic relationship between field and theoretical results obtained from the Canadian Capacity Guide is the best
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".