Factors Affecting Road Capacity Under non-Ideal Conditions in Egypt
Bibliographic record
Abstract
The The road capacity reduction is the biggest problem that faces many developed countries specially Egypt. It has caused many problems in the highway traffic congestion and delays. The road capacity value drops due to various non-ideal conditions which includes changes in speed or travel time, traffic interruptions or restriction etc. In this study, the factors that cause road capacity reduction under the non-ideal conditions in Egypt such as lane width (FLW), heavy vehicle (FHV), driver population (FDP) and environmental factor (FE) are discussed and simulated through a case study using SYNCHRO software. The studied factors are divided into categories. The first group includes 8 factors that are part of the software and excluded from the capacity calculation equation and consequently, are substituted by the value of 1 in the capacity calculation. The second group includes 3 factors that are inserted in the capacity equation using various values. The third group includes 1 calibration factor (driver population) which needs to be adjusted to get the real field capacity. Different values of this factor are tried in the simulation until the traffic conditions are visually close to the reality. The optimum value of the calibration factor has been obtained as 0.817. Finally, incidents, which is a variable and unexpected factor, and occurs in non-conventional conditions has been studied. The capacity reduction caused by incidents was best modeled as a random variable, not a deterministic value, as is the current practice. Key words: Road Capacity; Traffic Congestion; Non-Ideal Conditions; Incidents Factor; Driver Population Factor; Lane Width Factor.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".