Impacts of Signal Free Corridors on the Incidence of Road Traffic Accidents in Karachi
Bibliographic record
Abstract
Increase in road Traffic Accidents is a global phenomenon and Pakistan is no exception. In Karachi, this problem becomes severe due to rapid growth of population. This menace is ruining the lives of thousands of people and making Karachi a worrisome place to live. Recent road geometrical transitions in the city with insufficient accessories have been reducing the problem of traffic congestion to a limited extent. On the contrary, this has erupted as a problem of Road Traffic Accidents which is reaching out of proportions. Recently, induction of a unique feature named, Signal Free Corridor in Karachi has cost many precious lives. In this paper GIS based analysis has been employed by using buffer technique to document the number of Road Traffic Accidents on four Signal Free Corridors, evaluated for five different years. It was revealed that the minor injury accidents were highest in all four Signal Free Corridors. However, in some cases number of severe and fatal road accident cases showed the emerging trend as well. Four types of road users were identified on these corridors out of which riders/pillion and pedestrians were the most vulnerable to Road Traffic Accidents. There is an emergent need to enforce the vehicle speed rules and regulations that would provide breathing time to traffic as well as reduce the incidents of consistent traffic blockages and rising Road Traffic Accidents.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".