Modeling Highway Traffic Safety in Nigeria Using Delphi Technique and Bayesian Network
Bibliographic record
Abstract
Highway traffic safety is an issue confronting developing countries and those of industrialized nations. Many people are injured and/or killed all over the world daily on highway traffic related crashes. Nigeria, as a developing country, has been experiencing unusually high traffic related injury and fatality rates. In order to commence combating highway safety problems in Nigeria, the first task is to identify the major contributing factors, however, Nigeria has no reliable and comprehensive database of traffic accidents and casualties. Consequently, the Delphi Technique was utilized in generating the required data such as number of registered automobiles, number of licensed drivers, and annual fatality count for modeling and forecasting accident rates in Nigeria. A Bayesian Network Model was developed and used, with the data obtained from Delphi process, to demonstrate possible traffic safety responses to different scenarios of changes in the Nigerian socio-political culture. Although the Delphi Technique and the Bayesian Network Model only estimate the accident and safety data, those methods can be a realistic option when those data are not available, especially for the developing countries. The conclusions that are drawn from the output of the Bayesian Network and the analysis of the research results support the notion that the major accident contributors have been identified and the top three contributors – Road Condition, driving under the influence (DUI), and Reckless Driving - are policy related. The Nigerian traffic safety outlook would improve significantly if the existing laws and policies can be enforced, even at a very moderate level.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".