Vehicle Accident Report Application for Solving Traffic Problems and Reduce the Ratio of Pollution using Case Study: Kuwait City
Bibliographic record
Abstract
Minor traffic accidents have become a major problem facing the road users in the recent years, according to the statistics from the Ministry of Interior (MOI) in Kuwait there were recorded 80,388 accidents by the year 2014. Accidents not only affect the mobility but also contribute to air pollution and slow down economic growth. These effects are the result of the seriously extended trips travel time due to accumulated vehicles queue. In some accidents cases, the lost time waiting for the arrival of the traffic officers and filling up the accident report could take up to 45 minutes. The new idea of Vehicle Accident Report application (I-VAR) concept developed by the research team would reduce the waiting time up to 3 minutes (93% savings), which would increase the level of service of the segment of a roadway. In addition, the study will be discussed four major situations on some of the busiest roads in Kuwait. Specifically, gas emissions and cost estimation. Improve the pollution obviously, by using the (I-VAR) application for the minor accidents there is an amount of 360,776,460 K.D would be saved yearly from the Kuwait government funds. It is a consequence of the huge savings in alleviating traffic congestion and generally produces more saver and efficient travel conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".