Impact of Pedestrians Crossing Road Width on Vehicles Traffic Flow at IJP Road
Bibliographic record
Abstract
In the developing countries, mostly pedestrians are involved in traffic accidents. These accidents occur due to the non-abidance of rules by both pedestrians and drivers. To quantify pedestrians crossing a road width impact on vehicular flow, a case study has been conducted at Inter Junction Principal road in Rawalpindi, Pakistan. Pedestrian crossing road width impact (of single, two, three and four pedestrians) has been calculated on vehicular flow, speed and density. For this purpose, roadside traffic video was recorded three times a day from a pedestrian bridge for eight consecutive days. For traffic flow analysis, Camlytics (commercial traffic analysis software) has been employed. Pedestrian’s road width crossing time, speed and impact on different types of vehicles have been analyzed according to pedestrian’s age and gender. Furthermore, road crossing impacts on vehicular flow, speed and density are analyzed by pedestrian’s age and gender. Relationships are developed between speed and flow, speed and density, and flow and density by the disturbances caused by pedestrian road crossing. It was observed that old female pedestrians cause the most vehicular flow disturbances. Furthermore, the speed distribution does not follow the Greenshield speed distribution characterization. The traffic speed does not reduce to zero even when there is maximum traffic density present on the road.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".