Role of Intelligence Transport System in the Fight against Road Accidents in Kenya
Bibliographic record
Abstract
Accidents continue to claim many lives due to human errors that can be avoided by application of technology.Application of intelligent transport systems (ITS) in the Transport industry has gained momentum and some government funded projects have been rolled out in countries like United States, United Kingdom, and Canada among others.However, ITS projects can be very costly and unattainable to developing countries if the right approach is not followed.Perhaps, this explains the reason why many developing countries are yet to embrace the use of ITS despite reporting among the highest road accidents.This paper presents a review on ITS and solutions they can offer in reducing road accidents in developing countries with a focus of Kenya.This study assesses the impact of intelligent transportation systems in alleviating road accidents.Motivation behind this research is to identify ways in which application of ICT can be applied through affordable and effective ways to help the government and other transport stakeholders in getting a solution to the problem that are affecting the society.A review of intelligent transport systems shows that if they can be effectively applied, accidents can be greatly reduced.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".