Using Data Mining Models to Identify Major Factors Contributing to the Severity of Traffic Collisions for Different Groups in Saskatchewan, Canada
Bibliographic record
Abstract
There are many factors contributing to traffic collisions and their severity. Different major factors are likely to contribute to traffic collisions and their severity for different groups of people. Clearly identifying the major contributing factors to traffic collisions and their severity for different groups of people will assist highway safety improvement initiatives by improved facility design and educational program to address the needs due to the changes in demographics. The traffic collision data used in this study has been collected over the last 20 years on the rural highways and urban streets from Saskatchewan, Canada. In order to determine the major factors contributing to traffic collisions and their severity for different groups of people, we present a data mining model using ID3 and C4.5 decision tree algorithms to analyze the traffic collision data in this paper. The experiment results from this study will show that the developed data mining model using decision tree can effectively classify the major contributing factors to traffic collisions and their collision severity for different groups of people with good accuracy. The major contributing factors to traffic collisions and their severity for different groups of people are compared.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".