Prediction of Delay at Signalized Intersections in Erbil City
Bibliographic record
Abstract
Rapid development in the past few years which lead to an increase in the number of vehicles has caused the increase in the traffic volume and creation of congestion in the urban streets which lead to an increase in the number of intersections that caused congestion and delay in traffic. For this reason, this study is conducted to calculate time delay at five signalized intersections located on two major ring roads in Erbil City. The equations (time-dependent equations) that are adopted by three different official manuals, Highway Capacity Manual 2000 (HCM2000), Canadian Capacity Guide 1995 (ITE 1995) and Australian Capacity Guide 1995 (ARRB 1995) were used. From analysis and comparison of Observed Delay and above methods, it was found positive relationship represents weak relationship between Observed and ARRB, and HCM with adjusted R2 = 0.649 and 0.550 respectively, and strong relationship between Observed and ITE delays as the value of adjusted R2, is equal to (0.886). Also, there is a good correlation between field delay and the two proposed and calibrated (ITE and ARRB delay Formula) with adjusted R2 value for two models which are 0.885. For better performance, it is recommended that the Signal timing and geometrically redesigning the intersections should be taken into consideration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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; 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".